From 7795c4c4e3698257a4ec9a260c1eaa4f4f42c830 Mon Sep 17 00:00:00 2001 From: Gunnar Morling Date: Sun, 6 Dec 2020 23:10:02 +0100 Subject: [PATCH 01/14] WIP --- ...love-performance-regression-tests.asciidoc | 419 ++++++++++++++++++ .../continuous_perf_testing_events_in_jmc.png | Bin 0 -> 248114 bytes .../continuous_perf_testing_gc_original.png | Bin 0 -> 245468 bytes .../continuous_perf_testing_gc_regression.png | Bin 0 -> 295713 bytes .../continuous_perf_testing_tlab_in_jmc.png | Bin 0 -> 322879 bytes ...ontinuous_perf_testing_tlab_stacktrace.png | Bin 0 -> 357663 bytes 6 files changed, 419 insertions(+) create mode 100644 content/blog/how-i-learned-to-stop-worrying-and-love-performance-regression-tests.asciidoc create mode 100644 static/images/continuous_perf_testing_events_in_jmc.png create mode 100644 static/images/continuous_perf_testing_gc_original.png create mode 100644 static/images/continuous_perf_testing_gc_regression.png create mode 100644 static/images/continuous_perf_testing_tlab_in_jmc.png create mode 100644 static/images/continuous_perf_testing_tlab_stacktrace.png diff --git a/content/blog/how-i-learned-to-stop-worrying-and-love-performance-regression-tests.asciidoc b/content/blog/how-i-learned-to-stop-worrying-and-love-performance-regression-tests.asciidoc new file mode 100644 index 0000000..79b7eb4 --- /dev/null +++ b/content/blog/how-i-learned-to-stop-worrying-and-love-performance-regression-tests.asciidoc @@ -0,0 +1,419 @@ +--- +title: "How I Stopped Worrying and Love Continuous Performance Regression Tests" +date: 2020-11-28T17:45:00+01:00 +draft: false +--- +:source-highlighter: rouge +:rouge-style: base16.dark +:icons: font +:imagesdir: /images +ifdef::env-github[] +:imagesdir: ../../static/images +endif::[] + +Functional unit and integration tests are a standard tool of any software development organization, +helping not only to ensure correctness of newly implemented code, +but also to identify regressions -- bugs to existing functionality introduced by a code change. +The situation looks different though when it comes to regressions related to non-functional requirements, in particular performance-related ones: +How to detect increased response times in a web application? +How to identify decreased through-put? + +These aspects are hard to test in an automated and reliable way in the development workflow, +as they are dependent on the underlying hardware. +E.g. assertions on the duration of specific requests of a web application cannot be run in a meaningful way on a developer laptop which differs from the actual production hardware. +When run in a virtualized or containerized CI environments, such tests are are prone to failures due to concurrent load of other applications and jobs. + +This post introduces https://github.com/gunnarmorling/jfrunit[JfrUnit], which offers a fresh angle to this topic by supporting assertions not on metrics like latency/throughput themselves, but on indirect metrics which may impact those. Based on https://openjdk.java.net/jeps/328[JDK Flight Recorder events], JfrUnit allows you define and execute assertions e.g. against expected memory allocation, database I/O, or number of executed SQL statements, for a defined workload. Starting off from a defined base line, future failures of such assertions are indicators for potential performance regressions in an application, as a code change may have introduced higher GC pressure, the retrieval of unneccessary data from the database, or common SQL problems like N+1 SELECT statements. + + + +JfrUnit provide means of identifying and analysizing such issues in a reliable, environment independent way in standard JUnit tests, before they manifest as performance regressions in production. +Test results are independent from wall clock time and thus provide actionable information also when not testing with production-like hardware and data volumes. + +This post is the first one in a series of three. +Part one provides an overview over continuous performance unit testing with JfrUnit, +based on the examples of asserting expected memory allocation and database I/O. +The second part will discuss the assertion of the SQL statements executed by an application, +based on the instrumentation of JDBC APIs using JMC Agent. +In the third and last part we'll explore how to keep historic performance test metrics, +allowing to identify trends in these metrics over a longer period of time. + +== Getting Started With JfrUnit + +JfrUnit is an extension for JUnit 5, which integrates Flight Recorder into unit tests; +it makes it straight forward to initiate a JFR recording including a given set of event types, +execute some test routine, and then assert the JFR events which should be produced. + +Here is a basic example of a JfrUnit test: + +[source,java] +---- +@JfrEventTest // <1> +public class JfrUnitTest { + + public JfrEvents jfrEvents = new JfrEvents(); + + @Test + @EnableEvent("jdk.GarbageCollection") // <2> + @EnableEvent("jdk.ThreadSleep") + public void shouldHaveGcAndSleepEvents() throws Exception { + System.gc(); + Thread.sleep(1000); + + jfrEvents.awaitEvents(); // <3> + + ExpectedEvent = event("jdk.GarbageCollection"); // <4> + assertThat(jfrEvents).contains(event); + + ExpectedEvent = event("jdk.GarbageCollection") // <4> + .with("cause", "System.gc()")); + assertThat(jfrEvents).contains(event); + + ExpectedEvent = event("jdk.ThreadSleep"). + with("time", Duration.ofSeconds(1))); + assertThat(jfrEvents).contains(event); + + assertThat(jfrEvents.ofType("jdk.GarbageCollection")).hasSize(1); // <5> + } +} +---- +<1> `@JfrEventTest` marks this as a JfrUnit test, activating its extension +<2> All JFR event types to be recorded must be enabled via `@EnableEvent` +<3> After running the test code, `awaitEvents()` must be invoked as a synchronization barrier, +making sure all previously produced events have been received +<4> Using the `JfrEventsAssert#event()` method, an `ExpectedEvent` instance can be created, optionally containing a set of expected attribute values, which then is asserted via `JfrEventsAssert#assertThat()` +<5> `JfrEvents#ofType()` allows to filter on specific event types, enabling arbitrary assertions on the returned stream of ``RecordedEvent``s + +As we'll see in a bit, JfrUnit integrates nicely with the Java streams API, +simplifying the filtering and aggregation of recorded events before matching them against asserted values. +It also persists the JFR recording of each test method, +so you can examine it after a test failure. + +To learn more about JfrUnit and its capabilities, take a look at the project's https://github.com/gunnarmorling/jfrunit[README]. +The project is in an early proof-of-concept stage at the moment, +so changes to its APIs and semantics are likely. + +Now that you've taken the JfrUnit quick tour, let's put that knowledge into practice. +Our example project will be the todo management Quarkus application you may already be familiar with from my earlier post about custom JFR events. +We're going to discuss multiple examples for using JfrUnit to identify potential performance regressions. + +== Spotting Increased Memory Allocation + +At first, let's explore how to identify increased memory allocation rates. +Typically, it's mostly library and middleware authors who are interested in this, +because for a library such as Hibernate ORM it can make a huge difference whether a method invoked many times on a hot code path allocates a few bytes more or less memory. +Allocating less memory means less work for the garbage collector, +which in turn means those precious CPU cores can spend more cycles processing your actual business logic. + +But also for an actual application itself it can be beneficial to keep an eye -- and systematically track -- object allocations, +as regressions there lead to increased GC pressure in turn to higher latencies and reduced through-put. + +The key for tracking object allocations with JFR are the `jdk.ObjectAllocationInNewTLAB` and `jdk.ObjectAllocationOutsideTLAB` events, +which are emitted when + +* Object allocation triggered the creation of a new thread-local allocation buffer (TLAB) +* An object got allocated outside of the threads TLAB + +[NOTE] +.Thread-local allocation buffers (TLAB) +==== +When creating new object instances on the heap, +this primarily happens via _thread-local allocation buffers_. +A TLAB is a pre-allocated memory block that's exclusively used by a single thread. +Creating new objects within a TLAB can happen without costly synchronization with other threads. +Once a thread's current TLAB capacity is about to be exceeded with a new object allocation, +a new TLAB will be allocated for that thread. +In addition, large objects can also be directly allocated, outside of the TLAB. + +To learn more about TLAB allocation, refer to https://shipilev.net/jvm/anatomy-quarks/4-tlab-allocation/[part #4] of Aleksey Shipilёv's "JVM Anatomy Quark" blog series. +==== + +Note these events don't allow for tracking of each individual object allocation, +as multiple objects will be allocated within a TLAB before a new one is required and thus the `jdk.ObjectAllocationInNewTLAB` event will be emitted. +But as that event exposes the size of the new TLAB, we can keep track of the overall amount of memory that's allocated. + +In that sense, `jdk.ObjectAllocationInNewTLAB` represents a sampling of object allocations, +which means we need to collect a reasonable number of events to identify those locations in the program which are the sources of high memory allocation and thus trigger new TLAB creation often. + +So let's start and work on a test that'll spot regressions in terms of object allocations of one of the todo app's API methods, `GET /todo/{id}`. +To identify a baseline of the allocation to be expected, +we first invoke that method in a loop and print out the actual allocation values. +This should happen in intervals, e.g. every 10,000 invocations, +so to average out numbers from individual calls. + +[source, java] +---- +@Test +@EnableEvent("jdk.ObjectAllocationInNewTLAB") // <1> +@EnableEvent("jdk.ObjectAllocationOutsideTLAB") +public void retrieveTodoBaseline() throws Exception { + Random r = new Random(); + + HttpClient client = HttpClient.newBuilder() + .build(); + + for (int i = 1; i<= 100_000; i++) { + executeRequest(r.nextInt(20) + 1, client); + + if (i % 10_000 == 0) { + jfrEvents.awaitEvents(); // <2> + + long sum = jfrEvents.filter(this::isObjectAllocationEvent) // <3> + .filter(this::isRelevantThread) + .mapToLong(this::getAllocationSize) + .sum(); + + System.out.printf( + Locale.ENGLISH, + "Requests executed: %s, memory allocated: (%,d bytes/request)%n", + i, sum/10_000 + ); + + jfrEvents.reset(); // <4> + } + } + + private void executeRequest(Random r, HttpClient client) throws Exception { + int id = r.nextInt(20) + 1; + + HttpRequest request = HttpRequest.newBuilder() + .uri(new URI("http://localhost:8081/todo/" + id)) + .headers("Content-Type", "application/json") + .GET() + .build(); + + HttpResponse response = client + .send(request, HttpResponse.BodyHandlers.ofString()); + + assertThat(response.statusCode()).isEqualTo(200); + } + + private boolean isObjectAllocationEvent(RecordedEvent re) { // <5> + String name = re.getEventType().getName(); + return name.equals("jdk.ObjectAllocationInNewTLAB") || + name.equals("jdk.ObjectAllocationOutsideTLAB"); + } + + private long getAllocationSize(RecordedEvent re) { // <6> + return re.getEventType().getName() + .equals("jdk.ObjectAllocationInNewTLAB") ? + re.getLong("tlabSize") : + re.getLong("allocationSize"); + } + + private boolean isRelevantThread(RecordedEvent re) { // <7> + return re.getThread().getJavaName().startsWith("vert.x-eventloop") || + re.getThread().getJavaName().startsWith("executor-thread"); + } +} +---- +<1> Enable the `jdk.ObjectAllocationInNewTLAB` and `jdk.ObjectAllocationOutsideTLAB` JFR events +<2> Every 10,000 events, wait for all the JFR events +<3> Calculate the allocated memory, by summing up the TLAB allocations of all relevant threads by filtering out the TLAB events of the web application's relevant threads +<4> Reset the event stream for the next iteration +<5> Is this a TLAB event? +<6> Get the new TLAB size in case of a newly allocated TLAB, otherwise the out of TLAB allocated object size +<7> We're only interested in the web application's own threads, in particular ignoring the main thread which runs the HTTP client of the test + +Here are the numbers I got from running 100,000 invocations: + +[source] +---- +Requests executed: 10000, memory allocated: 34096 bytes/request +Requests executed: 20000, memory allocated: 31768 bytes/request +Requests executed: 30000, memory allocated: 31473 bytes/request +Requests executed: 40000, memory allocated: 31462 bytes/request +Requests executed: 50000, memory allocated: 31547 bytes/request +Requests executed: 60000, memory allocated: 31545 bytes/request +Requests executed: 70000, memory allocated: 31537 bytes/request +Requests executed: 80000, memory allocated: 31624 bytes/request +Requests executed: 90000, memory allocated: 31703 bytes/request +Requests executed: 100000, memory allocated: 31682 bytes/request +---- + +As we see, there's some warm-up phase during allocation rates still get down, +but after ~20 K requests, the allocation per request is fairly stable, +with a volatility of ~1% when averaged out over 10K requests. +I.e. this initial phase should be excluded during the actual test. + +To emphasize the key part again, this allocation is per _request_, it is independent from wall clock time and thus is not dependent on the machine running the test (i.e. the test should behave the same when running on a developer laptop and on a CI machine), nor is it subject to volatility induced by other workloads running concurrently. + +Based on that, the actual test could look like so: + +[source, java] +---- +@Test +@EnableEvent("jdk.ObjectAllocationInNewTLAB") +@EnableEvent("jdk.ObjectAllocationOutsideTLAB") +public void retrieveTodo() throws Exception { + Random r = new Random(); + HttpClient client = HttpClient.newBuilder().build(); + + for (int i = 1; i<= 20_000; i++) { // <1> + executeRequest(r, client); + } + + jfrEvents.awaitEvents(); + jfrEvents.reset(); + + for (int i = 1; i<= 10_000; i++) { // <2> + executeRequest(r, client); + } + + jfrEvents.awaitEvents(); + + long sum = jfrEvents.filter(this::isObjectAllocationEvent) + .filter(this::isRelevantThread) + .mapToLong(this::getAllocationSize) + .sum(); + + assertThat(sum / 10_000).isLessThan(33_000); // <3> +} +---- +<1> Warm-up phase +<2> The actual test phase +<3> Assert the memory allocation per request is within the expected boundary + +Now let's assume we've wrapped up the initial round of work on this application, and its tests have been passing on CI for a while. +One day, the `retrieveTodo()` performance test method fails though: + +[source] +---- +java.lang.AssertionError: +Expecting: + <388370L> +to be less than: + <33000L> +---- + +Wow, it's suddenly allocating more than 10 times more memory per request than before! +What has happened? +To find the answer, we can take a look at the test's JFR recording, which JfrUnit persists under _target/jfrunit_: + +[source,bash] +---- +ls target/jfrunit + +dev.morling.demos.quarkus.TodoResourcePerformanceTest-createTodo.jfr +dev.morling.demos.quarkus.TodoResourcePerformanceTest-retrieveTodo.jfr +---- + +Open the *.jfr file for the failing in JDK Mission Control (JMC) in order to analyse all the recorded events +(note that the recording will always contain some JfrUnit-internal events which are needed for synchronizing the recording stream and the events exposed to the test). + +When taking a look at the TLAB events of the application's executor thread, +the culprit is identified quickly; +a lot of the sampled TLAB allocations contain this stack trace: + +image::continuous_perf_testing_tlab_in_jmc.png[TLAB allocations in JDK Mission Control] + +Interesting, REST Assured loading a Jackson object mapper, what's going on there? +Here's the full stacktrace: + +image::continuous_perf_testing_tlab_stacktrace.png[Complete stacktrace of the TLAB allocation] + +So it seems a REST call to another service is made from within the `TodoResource#get(long)` method! +At this point we know where to look into the source code of the application: + +[source,java] +---- +@GET +@Transactional +@Produces(MediaType.APPLICATION_JSON) +@Path("/{id}") +public Response get(@PathParam("id") long id) throws Exception { + Todo res = Todo.findById(id); + + User user = RestAssured.given().port(8082) + .when() + .get("/users/" + res.userId) + .as(User.class); + + res.userName = user.name; + + return Response.ok() + .entity(res) + .build(); +} +---- + +It seems a developer on the team has been taking the microservices mantra a bit too far and invokes another service in order to obtain some additional data associated to the user who created the retrieved todo. + +While that's problematic on its own right due to the inherent coupling between the two services +(how should the todo service react if the user service isn't available?), +they made matters worse by using the https://rest-assured.io/[REST Assured API] as a REST client in a less than ideal way. +The APIs simplicity and elegance makes it a great choice for testing (and indeed that's its primary use case), +but this particular usage seems to be not such a good choice for production code. + +At this point you should ask yourself whether the increased allocation per request actually is a problem for your application or not. +For that it helps to run some tests on actual request latency and through-put on a production like environment. +If there's no impact based on the workload you have to process, +you might very well decide that additional allocations are well spent for your application's purposes. + +Increasing the allocation per request by a factor of 10 quite likely does not fall into this category, though. +At the very least, we should look into making the call of the User REST API in a more efficient way, +either by setting up REST Assured in a more suitable way, or by looking for an alternative REST client. + +It's also worth examining the applications garbage collection behavior. +In order to so, you can run the performance test method again, +either enabling all the GC-related JFR event types, or by enabling a pre-existing configuration +(as e.g. created with and exported from JMC): + +[source, java] +---- +@Test +@EnableConfiguration("profile") +public void retrieveTodo() throws Exception { + // ... +} +---- + +Open the recording in JMC, and you'll see there's a substantial amount of GC activity happening: + +image::continuous_perf_testing_gc_regression.png[Garbage collections after the performance regression] + +The difference to the GC behavior before this code change is striking: + +image::continuous_perf_testing_gc_original.png[Garbage collections before the performance regression] + +Pause times are worse, directly impacting the application's latency, and the largely increased GC volume means the environment will be able to serve less concurrent requests when reaching its capacity. +Meaning, you'd have to provision another machine earlier on as your load increases. +On a side note, there seems to be a memory leak before _and_ after the code change, as indicated by the ever increased heap size post GC, but I'll leave it for another time to analyze that. + +Now such drastic increase of allocation and impact on performance should hopefully be an exception rather than a regular situation. +But the example shows how continuous performance tests on metrics like memory allocation via Flight Recorder and JfrUnit can help to identify performance issues in an automated and reliable way, +preventing such regression to sneak into production. +Being able to identify this kind of issue by running tests locally on a developer laptop or a CI server, +can be a huge time-saver and productivity boost. + +== Identifying Increased I/O With the Database + +Once you've begun to look at performance tests through the lense of JfrUnit, +more and more possibilities pop up. +Asserting a maximum number of garbage collections? Not a problem. +Avoiding an unexpected amount of file system IO? The xyz event is our friend. +Examining and asserting the I/O done with the database? Easily doable. + +You can find a complete list of all JFR event types by JDK version in this https://bestsolution-at.github.io/jfr-doc/[nice matrix] created by https://twitter.com/tomsontom[Tom Schindl]. +The number of JFR event types is growing constantly, as of JDK 15, there 157 different ones of them. + +Now let's take a look at assertions on database I/O, as the amount of data fetched from the database often is a very impactful factor for an application's behavior. +A regression here, i.e. loading more data from the database than anticipated, may indicate that data is unnecessarily loaded, processing... + +== Summary and Outlook + +== TODO + +complement to perf tests + +How to spot unreasonably increased memory consumption or I/O? +How to identify bad-performing SQL statements introduced by a new feature implementation or a refactoring? + +spot regression after improvment (10 -> 60 -> 80) +won't help against worsened explain plan + +no silver bullet or magic, requires understanding and insight into the application + +You can find the https://github.com/gunnarmorling/jfr-custom-events/blob/master/example-service/src/test/java/dev/morling/demos/quarkus/TodoResourceTest.java[complete source code] of this test and the Postgres test resource on GitHub. + +_Many thanks to xyz for his feedback while writing this blog post!_ diff --git a/static/images/continuous_perf_testing_events_in_jmc.png b/static/images/continuous_perf_testing_events_in_jmc.png new file mode 100644 index 0000000000000000000000000000000000000000..04953fc6202e80051be2873571da1bc2db52be00 GIT binary patch literal 248114 zcmdqIWmH|uwl0bUcL?qh+zIaP?yeJpy9Eyfm*DR1?jC}Bkcs=m-JQqU`@DOy?mc_m z_Wr)v>c<#W)vIQ=>b<_I5vHUdi3E=a4+aK?BrPST0tN<+0|o}U4h#KW!#>zY3kC+? zU@0o9BrPgRqU2<6ZfOGm1Ct6%OoCDSsErkDsP2kf5RCO>YKKCq2zE3%AMBtd~j8n=a?0 zuBTHU^|K5FF!2E_cs8R?V60H+%3}VU?Ysvfirh znw9O>#``DmE3Q9$F%b%24n8F&c`_5nVfK?dCa8LE-ZiHqh7K{(dPJOrbm|D_oJX@(jH zHb^DG0$^?~?*KDWB<&X#f7zTOeVJn87M@XTmHM$anF-6h#`$e>`+j|7?1#dcu|Kj* 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Gunnar Morling Date: Mon, 7 Dec 2020 20:31:38 +0100 Subject: [PATCH 02/14] Misc. changes --- ...love-performance-regression-tests.asciidoc | 23 +++++++++++-------- 1 file changed, 14 insertions(+), 9 deletions(-) diff --git a/content/blog/how-i-learned-to-stop-worrying-and-love-performance-regression-tests.asciidoc b/content/blog/how-i-learned-to-stop-worrying-and-love-performance-regression-tests.asciidoc index 79b7eb4..df12569 100644 --- a/content/blog/how-i-learned-to-stop-worrying-and-love-performance-regression-tests.asciidoc +++ b/content/blog/how-i-learned-to-stop-worrying-and-love-performance-regression-tests.asciidoc @@ -23,20 +23,21 @@ as they are dependent on the underlying hardware. E.g. assertions on the duration of specific requests of a web application cannot be run in a meaningful way on a developer laptop which differs from the actual production hardware. When run in a virtualized or containerized CI environments, such tests are are prone to failures due to concurrent load of other applications and jobs. -This post introduces https://github.com/gunnarmorling/jfrunit[JfrUnit], which offers a fresh angle to this topic by supporting assertions not on metrics like latency/throughput themselves, but on indirect metrics which may impact those. Based on https://openjdk.java.net/jeps/328[JDK Flight Recorder events], JfrUnit allows you define and execute assertions e.g. against expected memory allocation, database I/O, or number of executed SQL statements, for a defined workload. Starting off from a defined base line, future failures of such assertions are indicators for potential performance regressions in an application, as a code change may have introduced higher GC pressure, the retrieval of unneccessary data from the database, or common SQL problems like N+1 SELECT statements. +This post introduces https://github.com/gunnarmorling/jfrunit[JfrUnit], which offers a fresh angle to this topic by supporting assertions not on metrics like latency/throughput themselves, but on indirect metrics which may impact those. Based on https://openjdk.java.net/jeps/328[JDK Flight Recorder] events, JfrUnit allows you define and execute assertions e.g. against expected memory allocation, database I/O, or number of executed SQL statements, for a defined workload. Starting off from a defined base line, future failures of such assertions are indicators for potential performance regressions in an application, as a code change may have introduced higher GC pressure, the retrieval of unneccessary data from the database, or common SQL problems like N+1 SELECT statements. JfrUnit provide means of identifying and analysizing such issues in a reliable, environment independent way in standard JUnit tests, before they manifest as performance regressions in production. Test results are independent from wall clock time and thus provide actionable information also when not testing with production-like hardware and data volumes. -This post is the first one in a series of three. -Part one provides an overview over continuous performance unit testing with JfrUnit, -based on the examples of asserting expected memory allocation and database I/O. -The second part will discuss the assertion of the SQL statements executed by an application, -based on the instrumentation of JDBC APIs using JMC Agent. -In the third and last part we'll explore how to keep historic performance test metrics, -allowing to identify trends in these metrics over a longer period of time. +This post is the first one in a series of three: + +* Part one provides an overview over continuous performance unit testing with JfrUnit, +based on the examples of asserting expected memory allocation and database I/O +* The second part will discuss the assertion of the SQL statements executed by an application, +based on the instrumentation of JDBC APIs using JMC Agent +* In the third and last part we'll explore how to keep historic performance test metrics, +allowing to identify trends in these metrics over a longer period of time == Getting Started With JfrUnit @@ -298,7 +299,7 @@ dev.morling.demos.quarkus.TodoResourcePerformanceTest-createTodo.jfr dev.morling.demos.quarkus.TodoResourcePerformanceTest-retrieveTodo.jfr ---- -Open the *.jfr file for the failing in JDK Mission Control (JMC) in order to analyse all the recorded events +Let's open the *.jfr file for the failing test in JDK Mission Control (JMC) in order to analyse all the recorded events (note that the recording will always contain some JfrUnit-internal events which are needed for synchronizing the recording stream and the events exposed to the test). When taking a look at the TLAB events of the application's executor thread, @@ -411,9 +412,13 @@ How to identify bad-performing SQL statements introduced by a new feature implem spot regression after improvment (10 -> 60 -> 80) won't help against worsened explain plan +cycle counter +metrics not meaningful by themselves, is it much? detect regressions no silver bullet or magic, requires understanding and insight into the application +https://bugs.openjdk.java.net/browse/JDK-8257602 + You can find the https://github.com/gunnarmorling/jfr-custom-events/blob/master/example-service/src/test/java/dev/morling/demos/quarkus/TodoResourceTest.java[complete source code] of this test and the Postgres test resource on GitHub. _Many thanks to xyz for his feedback while writing this blog post!_ From 895e191a84bfe03bee6e124c006b181721a7d4d6 Mon Sep 17 00:00:00 2001 From: Gunnar Morling Date: Mon, 7 Dec 2020 22:21:19 +0100 Subject: [PATCH 03/14] Database IO, WIP --- ...love-performance-regression-tests.asciidoc | 57 +++++++++++++++++-- 1 file changed, 53 insertions(+), 4 deletions(-) diff --git a/content/blog/how-i-learned-to-stop-worrying-and-love-performance-regression-tests.asciidoc b/content/blog/how-i-learned-to-stop-worrying-and-love-performance-regression-tests.asciidoc index df12569..7dff350 100644 --- a/content/blog/how-i-learned-to-stop-worrying-and-love-performance-regression-tests.asciidoc +++ b/content/blog/how-i-learned-to-stop-worrying-and-love-performance-regression-tests.asciidoc @@ -1,5 +1,6 @@ --- -title: "How I Stopped Worrying and Love Continuous Performance Regression Tests" +## title: "How I Stopped Worrying and Love Continuous Performance Regression Tests" +title: "Towards Continuous Performance Regression Tests" date: 2020-11-28T17:45:00+01:00 draft: false --- @@ -389,17 +390,65 @@ can be a huge time-saver and productivity boost. == Identifying Increased I/O With the Database -Once you've begun to look at performance tests through the lense of JfrUnit, +Once you've started to look at performance tests through the lense of JfrUnit, more and more possibilities pop up. Asserting a maximum number of garbage collections? Not a problem. -Avoiding an unexpected amount of file system IO? The xyz event is our friend. +Avoiding an unexpected amount of file system IO? The `jdk.FileRead` and `jdk.FileWrite` event is our friend. Examining and asserting the I/O done with the database? Easily doable. You can find a complete list of all JFR event types by JDK version in this https://bestsolution-at.github.io/jfr-doc/[nice matrix] created by https://twitter.com/tomsontom[Tom Schindl]. The number of JFR event types is growing constantly, as of JDK 15, there 157 different ones of them. Now let's take a look at assertions on database I/O, as the amount of data fetched from the database often is a very impactful factor for an application's behavior. -A regression here, i.e. loading more data from the database than anticipated, may indicate that data is unnecessarily loaded, processing... +A regression here, i.e. loading more data from the database than anticipated, may indicate that data is unnecessarily loaded, e.g. by selecting a set of data only to filter it in the application, instead of doing so via SQL in the database, +resulting in increased request durations. + +So how could such test look like for our `retrieveTodo()` call? +The general approach is the same as with memory allocations: +first define a baseline of the bytes read and written by invoking the API under test a defined number of executions. +Once that's done, you can implement the actual test, including an assertion on the expected number of bytes: + +[source, java] +---- +@Test +@EnableEvent(value="jdk.SocketRead", stackTrace=INCLUDED) // <1> +@EnableEvent(value="jdk.SocketWrite", stackTrace=INCLUDED) +public void retrieveTodo() throws Exception { + Random r = new Random(); + HttpClient client = HttpClient.newBuilder() + .build(); + + for (int i = 1; i<= ITERATIONS; i++) { + executeRequest(r.nextInt(20) + 1, client); + } + + jfrEvents.awaitEvents(); + + long count = jfrEvents.filter(this::isDatabaseIoEvent).count(); // <2> + assertThat(count / ITERATIONS).isEqualTo(4).describedAs("write + read per statement, write + read per commit"); + + long bytesReadOrWritten = jfrEvents.filter(this::isDatabaseIoEvent) + .mapToLong(this::getBytesReadOrWritten) + .sum(); + + assertThat(bytesReadOrWritten / ITERATIONS).isLessThan(250); // <3> +} + +private boolean isDatabaseIoEvent(RecordedEvent re) { // <4> + return ((re.getEventType().getName().equals("jdk.SocketRead") || + re.getEventType().getName().equals("jdk.SocketWrite")) && + re.getInt("port") == databasePort); +} + +private long getBytesReadOrWritten(RecordedEvent re) { // <5> + return re.getEventType().getName().equals("jdk.SocketRead") ? re.getLong("bytesRead") : re.getLong("bytesWritten"); +} +---- +<1> Enable the `jdk.SocketRead` and `jdk.SocketWrite` events; by default, those contain the stacktrace of the read or write event, so that needs to be enabled explicitly +<2> There should be four events per invocation +<3> Less than 250 bytes I/O are expected per invocation +<4> Only read and write events on the database port are relevant for this test, but e.g. not I/O on the web port of the application +<5> Retrieve the value of the `bytesRead` or `bytesWritten` field, depending on the event type == Summary and Outlook From 92f2acce1baf1c64664b6995136f092fba3c893d Mon Sep 17 00:00:00 2001 From: Gunnar Morling Date: Tue, 8 Dec 2020 22:29:09 +0100 Subject: [PATCH 04/14] More Database IO, WIP --- ...love-performance-regression-tests.asciidoc | 70 +++++++++++++++++- ...tinuous_perf_testing_socket_regression.png | Bin 0 -> 354772 bytes 2 files changed, 69 insertions(+), 1 deletion(-) create mode 100644 static/images/continuous_perf_testing_socket_regression.png diff --git a/content/blog/how-i-learned-to-stop-worrying-and-love-performance-regression-tests.asciidoc b/content/blog/how-i-learned-to-stop-worrying-and-love-performance-regression-tests.asciidoc index 7dff350..635ceac 100644 --- a/content/blog/how-i-learned-to-stop-worrying-and-love-performance-regression-tests.asciidoc +++ b/content/blog/how-i-learned-to-stop-worrying-and-love-performance-regression-tests.asciidoc @@ -380,7 +380,14 @@ image::continuous_perf_testing_gc_original.png[Garbage collections before the pe Pause times are worse, directly impacting the application's latency, and the largely increased GC volume means the environment will be able to serve less concurrent requests when reaching its capacity. Meaning, you'd have to provision another machine earlier on as your load increases. -On a side note, there seems to be a memory leak before _and_ after the code change, as indicated by the ever increased heap size post GC, but I'll leave it for another time to analyze that. + +[NOTE] +.Memory Leak in the JFR Event Streaming API +==== +The astute reader may have noticed that there is a memory leak before _and_ after the code change, as indicated by the ever increased heap size post GC. +After some exploration https://mail.openjdk.java.net/pipermail/hotspot-jfr-dev/2020-December/001972.html[it turned out] that this is a bug in the JFR event streaming API which holds on to all the `RecordedEvent` instances internally. +https://twitter.com/ErikGahlin[Erik Gahlin] from the OpenJDK team logged https://bugs.openjdk.java.net/browse/JDK-8257906[JDK-8257906] for tracking and hopefully fixing this in JDK 16. +==== Now such drastic increase of allocation and impact on performance should hopefully be an exception rather than a regular situation. But the example shows how continuous performance tests on metrics like memory allocation via Flight Recorder and JfrUnit can help to identify performance issues in an automated and reliable way, @@ -450,6 +457,65 @@ private long getBytesReadOrWritten(RecordedEvent re) { // <5> <4> Only read and write events on the database port are relevant for this test, but e.g. not I/O on the web port of the application <5> Retrieve the value of the `bytesRead` or `bytesWritten` field, depending on the event type +Now let's again assume that after some time the test begins to fail, this time it's the assertion on the number of executed reads and writes: + +[source] +---- +AssertionFailedError: +Expecting: + <18L> +to be equal to: + <4L> +but was not. +---- + +Also the number of bytes read and written has substantially increased: + +[source] +---- +java.lang.AssertionError: +Expecting: + <1117L> +to be less than: + <250L> +---- + +That's definitely something to look into, as increased database I/O implies reduced request latency. +So let's open the recording of the failed test in Flight Recorder and take a look at the socket read and write events. +Thanks to enabling stacktraces for the wotJFR event types we can quite quickly identify the events asssociated to an invocation of the `GET /todo/{id}` API: + +image::continuous_perf_testing_socket_regression.png[Socket read and write events after the performance regression] + +At this point, some familiarity with the application in question will come in handy to identify suspicous events. +But even without that, we could compare previous recordings of successful test runs with the recording from the failing one to see where differences are. +In the case at hand, the `BlobInputStream` and Hibernate's `BlobTypeDescriptor` in the call stack are unexpected, as our `User` entity didn't have any `BLOB` attribute before. +Comparing with the latest version and a look into the git history of that class confirms that there's a new attribute storing an image +(surely not a best practice to do so ;): + +[source] +---- +@Entity +public class Todo extends PanacheEntity { + + public String title; + public int priority; + public boolean completed; + + @Lob + public byte[] image; +} +---- + +We now would have to decide whether this image attribute should actually be loaded for this use case and thus should have to adjust the test accordingly, +or whether it'd for instance make more sense to mark this property as a lazily loaded one and only retrieve it when actually required. + +Solely working with the raw socket read and write events can be a bit cumbersome, though. +Wouldn't it be nice if we also has the actual SQL statement which caused this I/O? +Glad you asked! +Neither Hibernate nor the Postgres JDBC driver emit any JFR events at the moment, +but in part two of this blog post series, we'll discuss how to instrument an existing library to emit events like this, +using a Java agent, without modifying the library in question. 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Morling Date: Sat, 12 Dec 2020 10:18:41 +0100 Subject: [PATCH 05/14] Summary WIP --- ...love-performance-regression-tests.asciidoc | 35 +++++++++++++++++-- 1 file changed, 33 insertions(+), 2 deletions(-) diff --git a/content/blog/how-i-learned-to-stop-worrying-and-love-performance-regression-tests.asciidoc b/content/blog/how-i-learned-to-stop-worrying-and-love-performance-regression-tests.asciidoc index 635ceac..a04271d 100644 --- a/content/blog/how-i-learned-to-stop-worrying-and-love-performance-regression-tests.asciidoc +++ b/content/blog/how-i-learned-to-stop-worrying-and-love-performance-regression-tests.asciidoc @@ -341,6 +341,21 @@ public Response get(@PathParam("id") long id) throws Exception { It seems a developer on the team has been taking the microservices mantra a bit too far and invokes another service in order to obtain some additional data associated to the user who created the retrieved todo. +[NOTE] +.Tracking Object Allocations in Java 16 +==== +The two TLAB allocation events provide all the information required for analysing object allocations in Java applications, +but often it's not practical to enable them on a continuous basis when running in production. +Due to the high amount of events produced, enabling them adds some overhead in terms of latency, +also the size of JFR recording files can be hard to predict. + +Both issues are addressed by a https://bugs.openjdk.java.net/browse/JDK-8257602[JFR improvement] that's proposed for inclusion into Java 16, +"JFR Event Throttling". +This will provide control over the emission rate of events, e.g. allowing to sample object allocations with a defined rate of 100 events per second, +which addresses both the overhead as well as the recording size issue. +A new event type, `jdk.ObjectAllocationSample` will be added, too, which will be enabled in the JFR default configuration. +==== + While that's problematic on its own right due to the inherent coupling between the two services (how should the todo service react if the user service isn't available?), they made matters worse by using the https://rest-assured.io/[REST Assured API] as a REST client in a less than ideal way. @@ -518,8 +533,26 @@ using a Java agent, without modifying the library in question. == Summary and Outlook +JDK Flight Recorder and JfrUnit open up a very interesting approach for identifying potential performance regressions in Java applications. +By measuring and asserting metrics that _impact_ an application's performance characteristics, most notably latency and throughput, +rather than examining those primary metrics themselves, +you can implement stable and reliable automated tests, +whose outcome does not depend on the capabilities of the execution environment (e.g. number/size of CPUs) or other influential factors like + +Regressions in such impacting metrics, e.g. the amount of allocated memory or bytes read from an application's database, +are indicators that the application's performance has degraded. + +There are some advantages over performance tests on actual latency and throughput themselves: + +* You can identify potential regressions also when running tests on hardware different (less powerful) from the actual production hardware +* Tests are robust and not prone to factors like concurrent load on a CI server or a virtualized/containerized execution environment +* Certain categories of potential performance regressions can be identified also without running a production-like worklad + + + == TODO +locks complement to perf tests How to spot unreasonably increased memory consumption or I/O? @@ -532,8 +565,6 @@ cycle counter metrics not meaningful by themselves, is it much? detect regressions no silver bullet or magic, requires understanding and insight into the application -https://bugs.openjdk.java.net/browse/JDK-8257602 - don#t need many repetitions You can find the https://github.com/gunnarmorling/jfr-custom-events/blob/master/example-service/src/test/java/dev/morling/demos/quarkus/TodoResourceTest.java[complete source code] of this test and the Postgres test resource on GitHub. From ed986072d51f5d0d86bc18494a32ed0c7298e4aa Mon Sep 17 00:00:00 2001 From: Gunnar Morling Date: Mon, 14 Dec 2020 21:57:57 +0100 Subject: [PATCH 06/14] Towards Continuous Performance Regression Testing --- ...s-performance-regression-testing.asciidoc} | 270 ++++++++++-------- 1 file changed, 146 insertions(+), 124 deletions(-) rename content/blog/{how-i-learned-to-stop-worrying-and-love-performance-regression-tests.asciidoc => towards-continuous-performance-regression-testing.asciidoc} (60%) diff --git a/content/blog/how-i-learned-to-stop-worrying-and-love-performance-regression-tests.asciidoc b/content/blog/towards-continuous-performance-regression-testing.asciidoc similarity index 60% rename from content/blog/how-i-learned-to-stop-worrying-and-love-performance-regression-tests.asciidoc rename to content/blog/towards-continuous-performance-regression-testing.asciidoc index a04271d..79a5cc3 100644 --- a/content/blog/how-i-learned-to-stop-worrying-and-love-performance-regression-tests.asciidoc +++ b/content/blog/towards-continuous-performance-regression-testing.asciidoc @@ -1,7 +1,6 @@ --- -## title: "How I Stopped Worrying and Love Continuous Performance Regression Tests" -title: "Towards Continuous Performance Regression Tests" -date: 2020-11-28T17:45:00+01:00 +title: "Towards Continuous Performance Regression Testing" +date: 2020-12-16T17:45:00+01:00 draft: false --- :source-highlighter: rouge @@ -17,34 +16,36 @@ helping not only to ensure correctness of newly implemented code, but also to identify regressions -- bugs to existing functionality introduced by a code change. The situation looks different though when it comes to regressions related to non-functional requirements, in particular performance-related ones: How to detect increased response times in a web application? -How to identify decreased through-put? +How to identify decreased throughput? -These aspects are hard to test in an automated and reliable way in the development workflow, -as they are dependent on the underlying hardware. -E.g. assertions on the duration of specific requests of a web application cannot be run in a meaningful way on a developer laptop which differs from the actual production hardware. -When run in a virtualized or containerized CI environments, such tests are are prone to failures due to concurrent load of other applications and jobs. +These aspects are typically hard to test in an automated and reliable way in the development workflow, +as they are dependent on the underlying hardware and the workload of an application. +For instance assertions on the duration of specific requests of a web application cannot be run in a meaningful way on a developer laptop which differs from the actual production hardware. +When run in a virtualized or containerized CI environment, such tests are prone to failures due to concurrent load of other applications and jobs. -This post introduces https://github.com/gunnarmorling/jfrunit[JfrUnit], which offers a fresh angle to this topic by supporting assertions not on metrics like latency/throughput themselves, but on indirect metrics which may impact those. Based on https://openjdk.java.net/jeps/328[JDK Flight Recorder] events, JfrUnit allows you define and execute assertions e.g. against expected memory allocation, database I/O, or number of executed SQL statements, for a defined workload. Starting off from a defined base line, future failures of such assertions are indicators for potential performance regressions in an application, as a code change may have introduced higher GC pressure, the retrieval of unneccessary data from the database, or common SQL problems like N+1 SELECT statements. +This post introduces https://github.com/gunnarmorling/jfrunit[JfrUnit], which offers a fresh angle to this topic by supporting assertions not on metrics like latency/throughput themselves, but on _indirect metrics_ which may impact those. +Based on https://openjdk.java.net/jeps/328[JDK Flight Recorder] events, JfrUnit allows you define and execute assertions e.g. against expected memory allocation, database I/O, or number of executed SQL statements, for a given workload. +Starting off from a defined base line, future failures of such assertions are indicators for potential performance regressions in an application, as a code change may have introduced higher GC pressure, +the retrieval of unneccessary data from the database, or SQL problems commonly induced by ORM tools, like N+1 SELECT statements. -JfrUnit provide means of identifying and analysizing such issues in a reliable, environment independent way in standard JUnit tests, before they manifest as performance regressions in production. +JfrUnit provides means of identifying and analyzing such anomalies in a reliable, environment independent way in standard JUnit tests, +before they manifest as actual performance regressions in production. Test results are independent from wall clock time and thus provide actionable information also when not testing with production-like hardware and data volumes. -This post is the first one in a series of three: +This post is the first one in a series of two: * Part one provides an overview over continuous performance unit testing with JfrUnit, based on the examples of asserting expected memory allocation and database I/O * The second part will discuss the assertion of the SQL statements executed by an application, -based on the instrumentation of JDBC APIs using JMC Agent -* In the third and last part we'll explore how to keep historic performance test metrics, -allowing to identify trends in these metrics over a longer period of time +based on the instrumentation of JDBC APIs using https://wiki.openjdk.java.net/display/jmc/The+JMC+Agent[JMC Agent] == Getting Started With JfrUnit JfrUnit is an extension for JUnit 5, which integrates Flight Recorder into unit tests; -it makes it straight forward to initiate a JFR recording including a given set of event types, -execute some test routine, and then assert the JFR events which should be produced. +it makes it straight forward to initiate a JFR recording for a given set of event types, +execute some test routine, and then assert the JFR events which should have been produced. Here is a basic example of a JfrUnit test: @@ -81,40 +82,41 @@ public class JfrUnitTest { ---- <1> `@JfrEventTest` marks this as a JfrUnit test, activating its extension <2> All JFR event types to be recorded must be enabled via `@EnableEvent` -<3> After running the test code, `awaitEvents()` must be invoked as a synchronization barrier, +<3> After running the test logic, `awaitEvents()` must be invoked as a synchronization barrier, making sure all previously produced events have been received <4> Using the `JfrEventsAssert#event()` method, an `ExpectedEvent` instance can be created, optionally containing a set of expected attribute values, which then is asserted via `JfrEventsAssert#assertThat()` <5> `JfrEvents#ofType()` allows to filter on specific event types, enabling arbitrary assertions on the returned stream of ``RecordedEvent``s As we'll see in a bit, JfrUnit integrates nicely with the Java streams API, -simplifying the filtering and aggregation of recorded events before matching them against asserted values. -It also persists the JFR recording of each test method, -so you can examine it after a test failure. - +simplifying the filtering and aggregation of recorded events before matching them against expected values. +JfrUnit also persists the JFR recording of each test method, +so you can examine it after a test failure, +for instance using https://openjdk.java.net/projects/jmc/[JDK Mission Control]. To learn more about JfrUnit and its capabilities, take a look at the project's https://github.com/gunnarmorling/jfrunit[README]. The project is in an early proof-of-concept stage at the moment, so changes to its APIs and semantics are likely. Now that you've taken the JfrUnit quick tour, let's put that knowledge into practice. -Our example project will be the todo management Quarkus application you may already be familiar with from my earlier post about custom JFR events. +Our example project will be the https://github.com/gunnarmorling/jfr-custom-events[todo management] Quarkus application you may already be familiar with from my link:/blog/rest-api-monitoring-with-custom-jdk-flight-recorder-events/[earlier post] about custom JFR events. We're going to discuss multiple examples for using JfrUnit to identify potential performance regressions. == Spotting Increased Memory Allocation At first, let's explore how to identify increased memory allocation rates. Typically, it's mostly library and middleware authors who are interested in this, -because for a library such as Hibernate ORM it can make a huge difference whether a method invoked many times on a hot code path allocates a few bytes more or less memory. +since it can make a huge difference for a library such as Hibernate ORM whether a method invoked many times on a hot code path allocates a few bytes more or less memory. Allocating less memory means less work for the garbage collector, -which in turn means those precious CPU cores can spend more cycles processing your actual business logic. +which in turn means those precious CPU cores of your machine can spend more cycles processing your actual business logic. -But also for an actual application itself it can be beneficial to keep an eye -- and systematically track -- object allocations, -as regressions there lead to increased GC pressure in turn to higher latencies and reduced through-put. +But also for an actual application itself it can be beneficial to keep an eye on -- and systematically track -- object allocations, +as regressions there lead to increased GC pressure, +and in turn eventually to higher latencies and reduced throughput. The key for tracking object allocations with JFR are the `jdk.ObjectAllocationInNewTLAB` and `jdk.ObjectAllocationOutsideTLAB` events, which are emitted when -* Object allocation triggered the creation of a new thread-local allocation buffer (TLAB) -* An object got allocated outside of the threads TLAB +* an object allocation triggered the creation of a new thread-local allocation buffer (TLAB) +* an object got allocated outside of the threads TLAB [NOTE] .Thread-local allocation buffers (TLAB) @@ -123,7 +125,7 @@ When creating new object instances on the heap, this primarily happens via _thread-local allocation buffers_. A TLAB is a pre-allocated memory block that's exclusively used by a single thread. Creating new objects within a TLAB can happen without costly synchronization with other threads. -Once a thread's current TLAB capacity is about to be exceeded with a new object allocation, +Once a thread's current TLAB capacity is about to be exceeded by a new object allocation, a new TLAB will be allocated for that thread. In addition, large objects can also be directly allocated, outside of the TLAB. @@ -132,12 +134,12 @@ To learn more about TLAB allocation, refer to https://shipilev.net/jvm/anatomy- Note these events don't allow for tracking of each individual object allocation, as multiple objects will be allocated within a TLAB before a new one is required and thus the `jdk.ObjectAllocationInNewTLAB` event will be emitted. -But as that event exposes the size of the new TLAB, we can keep track of the overall amount of memory that's allocated. +But as that event exposes the size of the new TLAB, we can keep track of the overall amount of memory that's allocated while the application is running. In that sense, `jdk.ObjectAllocationInNewTLAB` represents a sampling of object allocations, -which means we need to collect a reasonable number of events to identify those locations in the program which are the sources of high memory allocation and thus trigger new TLAB creation often. +which means we need to collect a reasonable number of events to identify those locations in the program which are the sources of high memory allocation and thus frequently trigger new TLAB creations. -So let's start and work on a test that'll spot regressions in terms of object allocations of one of the todo app's API methods, `GET /todo/{id}`. +So let's start and work on a test for spotting regressions in terms of object allocations of one of the todo app's API methods, `GET /todo/{id}`. To identify a baseline of the allocation to be expected, we first invoke that method in a loop and print out the actual allocation values. This should happen in intervals, e.g. every 10,000 invocations, @@ -155,7 +157,7 @@ public void retrieveTodoBaseline() throws Exception { .build(); for (int i = 1; i<= 100_000; i++) { - executeRequest(r.nextInt(20) + 1, client); + executeRequest(r, client); if (i % 10_000 == 0) { jfrEvents.awaitEvents(); // <2> @@ -211,10 +213,10 @@ public void retrieveTodoBaseline() throws Exception { ---- <1> Enable the `jdk.ObjectAllocationInNewTLAB` and `jdk.ObjectAllocationOutsideTLAB` JFR events <2> Every 10,000 events, wait for all the JFR events -<3> Calculate the allocated memory, by summing up the TLAB allocations of all relevant threads by filtering out the TLAB events of the web application's relevant threads +<3> Calculate the allocated memory, by summing up the TLAB allocations of all relevant threads <4> Reset the event stream for the next iteration <5> Is this a TLAB event? -<6> Get the new TLAB size in case of a newly allocated TLAB, otherwise the out of TLAB allocated object size +<6> Get the new TLAB size in case of a newly allocated TLAB, otherwise the allocated object size out of TLAB <7> We're only interested in the web application's own threads, in particular ignoring the main thread which runs the HTTP client of the test Here are the numbers I got from running 100,000 invocations: @@ -233,12 +235,29 @@ Requests executed: 90000, memory allocated: 31703 bytes/request Requests executed: 100000, memory allocated: 31682 bytes/request ---- -As we see, there's some warm-up phase during allocation rates still get down, +As we see, there's some warm-up phase during which allocation rates still get down, but after ~20 K requests, the allocation per request is fairly stable, with a volatility of ~1% when averaged out over 10K requests. -I.e. this initial phase should be excluded during the actual test. +This means that this initial phase should be excluded during the actual test. -To emphasize the key part again, this allocation is per _request_, it is independent from wall clock time and thus is not dependent on the machine running the test (i.e. the test should behave the same when running on a developer laptop and on a CI machine), nor is it subject to volatility induced by other workloads running concurrently. +[NOTE] +.Tracking Object Allocations in Java 16 +==== +The two TLAB allocation events provide all the information required for analysing object allocations in Java applications, +but often it's not practical to enable them on a continuous basis when running in production. +Due to the high amount of events produced, enabling them adds some overhead in terms of latency, +also the size of JFR recording files can be hard to predict. + +Both issues are addressed by a https://bugs.openjdk.java.net/browse/JDK-8257602[JFR improvement] that's proposed for inclusion into Java 16, +"JFR Event Throttling". +This will provide control over the emission rate of events, e.g. allowing to sample object allocations with a defined rate of 100 events per second, +which addresses both the overhead as well as the recording size issue. +A new event type, `jdk.ObjectAllocationSample` will be added, too, which will be enabled in the JFR default configuration. +==== + +To emphasize the key part again, this allocation is per _request_, it is independent from wall clock time and thus is not dependent on the machine running the test +(i.e. the test should behave the same when running on a developer laptop and on a CI machine), +nor is it subject to volatility induced by other workloads running concurrently. Based on that, the actual test could look like so: @@ -274,7 +293,9 @@ public void retrieveTodo() throws Exception { ---- <1> Warm-up phase <2> The actual test phase -<3> Assert the memory allocation per request is within the expected boundary +<3> Assert the memory allocation per request is within the expected boundary; note we could also add a _lower_ boundary, +so to make sure we notice any future improvements (e.g. caused by upgrading to new efficient versions of a library), +which otherwise may hide subsequent regressions Now let's assume we've wrapped up the initial round of work on this application, and its tests have been passing on CI for a while. One day, the `retrieveTodo()` performance test method fails though: @@ -288,7 +309,7 @@ to be less than: <33000L> ---- -Wow, it's suddenly allocating more than 10 times more memory per request than before! +Ugh, it's suddenly allocating about ten times more memory per request than before! What has happened? To find the answer, we can take a look at the test's JFR recording, which JfrUnit persists under _target/jfrunit_: @@ -339,42 +360,29 @@ public Response get(@PathParam("id") long id) throws Exception { } ---- -It seems a developer on the team has been taking the microservices mantra a bit too far and invokes another service in order to obtain some additional data associated to the user who created the retrieved todo. - -[NOTE] -.Tracking Object Allocations in Java 16 -==== -The two TLAB allocation events provide all the information required for analysing object allocations in Java applications, -but often it's not practical to enable them on a continuous basis when running in production. -Due to the high amount of events produced, enabling them adds some overhead in terms of latency, -also the size of JFR recording files can be hard to predict. +It seems a developer on the team has been taking the microservices mantra a bit too far, and has changed the code so it invokes another service in order to obtain some additional data associated to the user who created the retrieved todo. -Both issues are addressed by a https://bugs.openjdk.java.net/browse/JDK-8257602[JFR improvement] that's proposed for inclusion into Java 16, -"JFR Event Throttling". -This will provide control over the emission rate of events, e.g. allowing to sample object allocations with a defined rate of 100 events per second, -which addresses both the overhead as well as the recording size issue. -A new event type, `jdk.ObjectAllocationSample` will be added, too, which will be enabled in the JFR default configuration. -==== - -While that's problematic on its own right due to the inherent coupling between the two services +While that's problematic in its own right due to the inherent coupling between the two services (how should the todo service react if the user service isn't available?), they made matters worse by using the https://rest-assured.io/[REST Assured API] as a REST client in a less than ideal way. The APIs simplicity and elegance makes it a great choice for testing (and indeed that's its primary use case), but this particular usage seems to be not such a good choice for production code. At this point you should ask yourself whether the increased allocation per request actually is a problem for your application or not. -For that it helps to run some tests on actual request latency and through-put on a production like environment. +For that it helps to run some tests on actual request latency and throughput in a production like environment. If there's no impact based on the workload you have to process, you might very well decide that additional allocations are well spent for your application's purposes. -Increasing the allocation per request by a factor of 10 quite likely does not fall into this category, though. -At the very least, we should look into making the call of the User REST API in a more efficient way, +Increasing the allocation per request by a factor of ten in the described way quite likely does not fall into this category, though. +At the very least, we should look into making the call against the User REST API more efficiently, either by setting up REST Assured in a more suitable way, or by looking for an alternative REST client. +Of course the external API call just by itself adds to the request latency, +which is something we might want to avoid. It's also worth examining the applications garbage collection behavior. In order to so, you can run the performance test method again, -either enabling all the GC-related JFR event types, or by enabling a pre-existing configuration -(as e.g. created with and exported from JMC): +either enabling all the GC-related JFR event types, or by enabling a pre-existing JFR configuration +(the JDK comes with two built-in JFR configurations, and you can also create and export them via JMC): [source, java] ---- @@ -393,19 +401,19 @@ The difference to the GC behavior before this code change is striking: image::continuous_perf_testing_gc_original.png[Garbage collections before the performance regression] -Pause times are worse, directly impacting the application's latency, and the largely increased GC volume means the environment will be able to serve less concurrent requests when reaching its capacity. -Meaning, you'd have to provision another machine earlier on as your load increases. +Pause times are worse, directly impacting the application's latency, and the largely increased GC volume means the environment will be able to serve less concurrent requests when reaching its capacity limits, +meaning you'd have to provision another machine earlier on as your load increases. [NOTE] .Memory Leak in the JFR Event Streaming API ==== The astute reader may have noticed that there is a memory leak before _and_ after the code change, as indicated by the ever increased heap size post GC. -After some exploration https://mail.openjdk.java.net/pipermail/hotspot-jfr-dev/2020-December/001972.html[it turned out] that this is a bug in the JFR event streaming API which holds on to all the `RecordedEvent` instances internally. +After some exploration https://mail.openjdk.java.net/pipermail/hotspot-jfr-dev/2020-December/001972.html[it turned out] that this is a bug in the JFR event streaming API which holds on to a large number of `RecordedEvent` instances internally. https://twitter.com/ErikGahlin[Erik Gahlin] from the OpenJDK team logged https://bugs.openjdk.java.net/browse/JDK-8257906[JDK-8257906] for tracking and hopefully fixing this in JDK 16. ==== -Now such drastic increase of allocation and impact on performance should hopefully be an exception rather than a regular situation. -But the example shows how continuous performance tests on metrics like memory allocation via Flight Recorder and JfrUnit can help to identify performance issues in an automated and reliable way, +Now such drastic increase of allocation and thus potential impact on performance should hopefully be an exception rather than a regular situation. +But the example shows how continuous performance unit tests on impacting metrics like memory allocation via Flight Recorder and JfrUnit can help to identify performance issues in an automated and reliable way, preventing such regression to sneak into production. Being able to identify this kind of issue by running tests locally on a developer laptop or a CI server, can be a huge time-saver and productivity boost. @@ -415,20 +423,21 @@ can be a huge time-saver and productivity boost. Once you've started to look at performance tests through the lense of JfrUnit, more and more possibilities pop up. Asserting a maximum number of garbage collections? Not a problem. -Avoiding an unexpected amount of file system IO? The `jdk.FileRead` and `jdk.FileWrite` event is our friend. +Avoiding an unexpected amount of file system IO? The `jdk.FileRead` and `jdk.FileWrite` events are our friend. Examining and asserting the I/O done with the database? Easily doable. You can find a complete list of all JFR event types by JDK version in this https://bestsolution-at.github.io/jfr-doc/[nice matrix] created by https://twitter.com/tomsontom[Tom Schindl]. -The number of JFR event types is growing constantly, as of JDK 15, there 157 different ones of them. +The number of JFR event types is growing constantly, as of JDK 15, there are 157 different ones of them. -Now let's take a look at assertions on database I/O, as the amount of data fetched from the database often is a very impactful factor for an application's behavior. -A regression here, i.e. loading more data from the database than anticipated, may indicate that data is unnecessarily loaded, e.g. by selecting a set of data only to filter it in the application, instead of doing so via SQL in the database, +Now let's take a look at assertions on database I/O, as the amount of data fetched from the database or written to often is a very impactful factor for an enterprise application's behavior. +A regression here, e.g. fetching more data from the database than anticipated, may indicate that data is unnecessarily loaded, +for instance by selecting a set of data only to filter it in the application, instead of doing so via SQL in the database, resulting in increased request durations. -So how could such test look like for our `retrieveTodo()` call? -The general approach is the same as with memory allocations: -first define a baseline of the bytes read and written by invoking the API under test a defined number of executions. -Once that's done, you can implement the actual test, including an assertion on the expected number of bytes: +So how could such test look like for our `GET /todo/{id}` API call? +The general approach is the same as before with memory allocations: +first define a baseline of the bytes read and written by invoking the API under test for a given number of executions. +Once that's done, you can implement the actual test, including an assertion on the expected number of bytes read or written: [source, java] ---- @@ -441,7 +450,7 @@ public void retrieveTodo() throws Exception { .build(); for (int i = 1; i<= ITERATIONS; i++) { - executeRequest(r.nextInt(20) + 1, client); + executeRequest(r, client); } jfrEvents.awaitEvents(); @@ -467,12 +476,13 @@ private long getBytesReadOrWritten(RecordedEvent re) { // <5> } ---- <1> Enable the `jdk.SocketRead` and `jdk.SocketWrite` events; by default, those contain the stacktrace of the read or write event, so that needs to be enabled explicitly -<2> There should be four events per invocation +<2> There should be four events per invocation of the API method <3> Less than 250 bytes I/O are expected per invocation <4> Only read and write events on the database port are relevant for this test, but e.g. not I/O on the web port of the application -<5> Retrieve the value of the `bytesRead` or `bytesWritten` field, depending on the event type +<5> Retrieve the value of the event's `bytesRead` or `bytesWritten` field, depending on the event type -Now let's again assume that after some time the test begins to fail, this time it's the assertion on the number of executed reads and writes: +Now let's again assume that after some time the test begins to fail. +This time it's the assertion on the number of executed reads and writes: [source] ---- @@ -495,19 +505,21 @@ to be less than: <250L> ---- -That's definitely something to look into, as increased database I/O implies reduced request latency. +That's definitely something to look into, as increased database I/O implies increased request latency. So let's open the recording of the failed test in Flight Recorder and take a look at the socket read and write events. -Thanks to enabling stacktraces for the wotJFR event types we can quite quickly identify the events asssociated to an invocation of the `GET /todo/{id}` API: +Thanks to enabling stacktraces for the two JFR event types we can quite quickly identify the events asssociated to an invocation of the `GET /todo/{id}` API: image::continuous_perf_testing_socket_regression.png[Socket read and write events after the performance regression] At this point, some familiarity with the application in question will come in handy to identify suspicous events. But even without that, we could compare previous recordings of successful test runs with the recording from the failing one to see where differences are. -In the case at hand, the `BlobInputStream` and Hibernate's `BlobTypeDescriptor` in the call stack are unexpected, as our `User` entity didn't have any `BLOB` attribute before. -Comparing with the latest version and a look into the git history of that class confirms that there's a new attribute storing an image +In the case at hand, the `BlobInputStream` and Hibernate's `BlobTypeDescriptor` in the call stack seem pretty unexpected, +as our `User` entity didn't have any `BLOB` attribute before. + +In reality, comparing with the latest version and a look into the git history of that class could confirm that there's a new attribute storing an image (surely not a best practice to do so ;): -[source] +[source,java] ---- @Entity public class Todo extends PanacheEntity { @@ -516,13 +528,15 @@ public class Todo extends PanacheEntity { public int priority; public boolean completed; - @Lob + @Lob // <1> public byte[] image; } ---- +<1> This looks suspicious! -We now would have to decide whether this image attribute should actually be loaded for this use case and thus should have to adjust the test accordingly, -or whether it'd for instance make more sense to mark this property as a lazily loaded one and only retrieve it when actually required. +We now would have to decide whether this image attribute should actually be loaded for this particular use case, +(if so, we'd have to adjust the test accordingly), +or whether it would for instance make more sense to mark this property as a lazily loaded one and only retrieve it when actually required. Solely working with the raw socket read and write events can be a bit cumbersome, though. Wouldn't it be nice if we also has the actual SQL statement which caused this I/O? @@ -531,42 +545,50 @@ Neither Hibernate nor the Postgres JDBC driver emit any JFR events at the moment but in part two of this blog post series, we'll discuss how to instrument an existing library to emit events like this, using a Java agent, without modifying the library in question. -== Summary and Outlook +== Discussion and Summary JDK Flight Recorder and JfrUnit open up a very interesting approach for identifying potential performance regressions in Java applications. -By measuring and asserting metrics that _impact_ an application's performance characteristics, most notably latency and throughput, -rather than examining those primary metrics themselves, -you can implement stable and reliable automated tests, -whose outcome does not depend on the capabilities of the execution environment (e.g. number/size of CPUs) or other influential factors like - -Regressions in such impacting metrics, e.g. the amount of allocated memory or bytes read from an application's database, -are indicators that the application's performance has degraded. - -There are some advantages over performance tests on actual latency and throughput themselves: - -* You can identify potential regressions also when running tests on hardware different (less powerful) from the actual production hardware -* Tests are robust and not prone to factors like concurrent load on a CI server or a virtualized/containerized execution environment -* Certain categories of potential performance regressions can be identified also without running a production-like worklad - - - -== TODO - -locks -complement to perf tests - -How to spot unreasonably increased memory consumption or I/O? -How to identify bad-performing SQL statements introduced by a new feature implementation or a refactoring? - -spot regression after improvment (10 -> 60 -> 80) -won't help against worsened explain plan -cycle counter - -metrics not meaningful by themselves, is it much? detect regressions -no silver bullet or magic, requires understanding and insight into the application - -don#t need many repetitions - -You can find the https://github.com/gunnarmorling/jfr-custom-events/blob/master/example-service/src/test/java/dev/morling/demos/quarkus/TodoResourceTest.java[complete source code] of this test and the Postgres test resource on GitHub. - -_Many thanks to xyz for his feedback while writing this blog post!_ +Instead of directly measuring an application's performance metrics, +most notably latency and throughput, +the idea is to measure and assert metrics that _impact_ the performance characteristics. +This allows you to implement stable and reliable automated performance unit tests, +whose outcome does not depend on the capabilities of the execution environment (e.g. number/size of CPUs), +or other influential factors like concurrently running programs. + +Regressions in such impacting metrics, e.g. the amount of allocated memory, or bytes read from an application's database, +are indicators that the application's performance may have been degraded. +This approach offers some interesting advantages over performance tests on actual latency and throughput themselves: + +* *Hardware independent:* You can identify potential regressions also when running tests on hardware which is different (i.e. less powerful) from the actual production hardware +* *Fast feedback cycle:* Being able to run performance regression tests on developer laptops, even in the IDE, allows for fast identification of potential regressions right during development, instead of having to wait for the results of less frequently executed test runs in a traditional performance test lab environment +* *Robustness:* Tests are robust and not prone to factors such as the load induced by parallel jobs of a CI server or a virtualized/containerized environment +* *Pro-active idenfication of performance issues:* Asserting a metric like memory allocation can help to identify future performance problems before they actual materialize; while the additional allocation rate may make no difference with the system's load as of today, it may negatively impact latency and throughput as the system reaches its limits with increased load; being able to identify the increased allocation rate early on allows for a more efficient handling of the situation while working on the code, compared to when finding out about such regression only later on +* *Reduced need for warm-up:* For traditional performance tests of Java-based applications, a thorough warm-up is mandatory, +e.g. to ensure proper optimization of the JIT-compiled code. In comparison, metrics like file or database I/O are very stable for a defined workload, so that regressions can be identified also with just a single or a few executions + +Needless to say, that you should be aware of the limitations of this approach, too: + +* *No statement on user-visible performance metrics:* Measuring and asserting performance-impacting factors doesn't tell you anything in terms of the user-visible performance characteristics themselves. +While we can reason about guarantees like "The system can handle 10K concurrent requests while the 99.9 percentile of requests has a latency of less than 250 ms", that's not the case for metrics like memory allocation or I/O. What does it mean if an application allocates 100 KB of RAM for a particular use case? Is it a lot? Too much? Just fine? +* *Focused on identifying regressions:* Somewhat related to the first point, this approach of testing is focused not on specific absolute values, but rather on identifying performance regressions. It's hard to tell whether 100 KB database I/O is good or bad for a particular web request, but a change from 100 KB to 200 KB might indicate that something is wrong +* *Focused on identifying _potential_ regressions:* A change in performance-impacting metrics does not necessarily imply an actual user-visible impacting performance regression. For instance it might be acceptable for a specific request to allocate more RAM than it did before, +if the production system generally isn't under high load and the additional GC effort doesn't matter in practice +* *Does not work for all performance-impacting metrics:* Some performance metrics cannot be meaningfully asserted in plain unit tests; +e.g. degraded throughput due to lock contention can typically only be identified with a reasonable number of concurrent requests +* *Only identifies regressions in the application itself:* A traditional integrative performance test of an enterprise application will also capture issues in related components, such as the application's database. A query run with a sub-optimal execution plan won't be noticed with this testing approach + +That being said, unit tests based on performance-impacting factors can be a very useful part of the performance testing strategy for an application. +They can help to identify potential performance regressions very early in the development lifecycle, +when they can be fixed comparatively easy and cheap. +Of course they are no silver bullet; +you should consider them as _complement_ for classic performance tests running on production-like hardware, not a _replacement_. + +The approach may feel a bit unfamiliar initially, +and it may take some time to learn about the different metrics which can be measured with JFR and asserted via JfrUnit, +as well as their implications on an application's performance characteristics. +But once this hurdle is passed, continuous performance regression tests can be a valuable tool in the box of every software and performance engineer. + +Stay tuned for part two of this blog post, where we'll explore how to trace the SQL statements executed by an application using the JMC Agent, +which will come in very handy for instance for identifying common performance problems like N+1 SELECT statements. + +_Many thanks to Hans-Peter Grahsl for his feedback while writing this blog post!_ From 0a2bc5f3f0cd76f993002903a7bb788d609d5c5c Mon Sep 17 00:00:00 2001 From: Gunnar Morling Date: Tue, 15 Dec 2020 17:13:20 +0100 Subject: [PATCH 07/14] Apply suggestions from code review Co-authored-by: Sanne Grinovero --- ...rds-continuous-performance-regression-testing.asciidoc | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/blog/towards-continuous-performance-regression-testing.asciidoc b/content/blog/towards-continuous-performance-regression-testing.asciidoc index 79a5cc3..1c9bb72 100644 --- a/content/blog/towards-continuous-performance-regression-testing.asciidoc +++ b/content/blog/towards-continuous-performance-regression-testing.asciidoc @@ -13,7 +13,7 @@ endif::[] Functional unit and integration tests are a standard tool of any software development organization, helping not only to ensure correctness of newly implemented code, -but also to identify regressions -- bugs to existing functionality introduced by a code change. +but also to identify regressions -- bugs in existing functionality introduced by a code change. The situation looks different though when it comes to regressions related to non-functional requirements, in particular performance-related ones: How to detect increased response times in a web application? How to identify decreased throughput? @@ -379,7 +379,7 @@ either by setting up REST Assured in a more suitable way, or by looking for an a Of course the external API call just by itself adds to the request latency, which is something we might want to avoid. -It's also worth examining the applications garbage collection behavior. +It's also worth examining the application's garbage collection behavior. In order to so, you can run the performance test method again, either enabling all the GC-related JFR event types, or by enabling a pre-existing JFR configuration (the JDK comes with two built-in JFR configurations, and you can also create and export them via JMC): @@ -555,14 +555,14 @@ This allows you to implement stable and reliable automated performance unit test whose outcome does not depend on the capabilities of the execution environment (e.g. number/size of CPUs), or other influential factors like concurrently running programs. -Regressions in such impacting metrics, e.g. the amount of allocated memory, or bytes read from an application's database, +Regressions in such impacting metrics, e.g. the amount of allocated memory, or bytes read from a database, are indicators that the application's performance may have been degraded. This approach offers some interesting advantages over performance tests on actual latency and throughput themselves: * *Hardware independent:* You can identify potential regressions also when running tests on hardware which is different (i.e. less powerful) from the actual production hardware * *Fast feedback cycle:* Being able to run performance regression tests on developer laptops, even in the IDE, allows for fast identification of potential regressions right during development, instead of having to wait for the results of less frequently executed test runs in a traditional performance test lab environment * *Robustness:* Tests are robust and not prone to factors such as the load induced by parallel jobs of a CI server or a virtualized/containerized environment -* *Pro-active idenfication of performance issues:* Asserting a metric like memory allocation can help to identify future performance problems before they actual materialize; while the additional allocation rate may make no difference with the system's load as of today, it may negatively impact latency and throughput as the system reaches its limits with increased load; being able to identify the increased allocation rate early on allows for a more efficient handling of the situation while working on the code, compared to when finding out about such regression only later on +* *Pro-active identification of performance issues:* Asserting a metric like memory allocation can help to identify future performance problems before they actual materialize; while the additional allocation rate may make no difference with the system's load as of today, it may negatively impact latency and throughput as the system reaches its limits with increased load; being able to identify the increased allocation rate early on allows for a more efficient handling of the situation while working on the code, compared to when finding out about such regression only later on * *Reduced need for warm-up:* For traditional performance tests of Java-based applications, a thorough warm-up is mandatory, e.g. to ensure proper optimization of the JIT-compiled code. In comparison, metrics like file or database I/O are very stable for a defined workload, so that regressions can be identified also with just a single or a few executions From 11f5b04b6f46a7571b623ccdc503d3b0c79c0231 Mon Sep 17 00:00:00 2001 From: Gunnar Morling Date: Tue, 15 Dec 2020 20:53:39 +0100 Subject: [PATCH 08/14] More review feedback --- ...us-performance-regression-testing.asciidoc | 55 ++++++++++++------- 1 file changed, 35 insertions(+), 20 deletions(-) diff --git a/content/blog/towards-continuous-performance-regression-testing.asciidoc b/content/blog/towards-continuous-performance-regression-testing.asciidoc index 1c9bb72..e00efe8 100644 --- a/content/blog/towards-continuous-performance-regression-testing.asciidoc +++ b/content/blog/towards-continuous-performance-regression-testing.asciidoc @@ -34,12 +34,14 @@ JfrUnit provides means of identifying and analyzing such anomalies in a reliable before they manifest as actual performance regressions in production. Test results are independent from wall clock time and thus provide actionable information also when not testing with production-like hardware and data volumes. -This post is the first one in a series of two: +This post is a bit longer than usual (I didn't have the time to write shorter ;), but it's broken down into several sections, +so you can take a break and continue later on with fresh energy: -* Part one provides an overview over continuous performance unit testing with JfrUnit, -based on the examples of asserting expected memory allocation and database I/O -* The second part will discuss the assertion of the SQL statements executed by an application, -based on the instrumentation of JDBC APIs using https://wiki.openjdk.java.net/display/jmc/The+JMC+Agent[JMC Agent] +* link:#_getting_started_with_jfrunit[Getting Started With JfrUnit] +* link:#_spotting_increased_memory_allocation[Spotting Increased Memory Allocation] +* link:#_identifying_increased_io_with_the_database[Identifying Increased I/O With the Database] +* link:#_discussion[Discussion] +* link:#_summary_and_outlook[Summary and Outlook] == Getting Started With JfrUnit @@ -97,14 +99,14 @@ The project is in an early proof-of-concept stage at the moment, so changes to its APIs and semantics are likely. Now that you've taken the JfrUnit quick tour, let's put that knowledge into practice. -Our example project will be the https://github.com/gunnarmorling/jfr-custom-events[todo management] Quarkus application you may already be familiar with from my link:/blog/rest-api-monitoring-with-custom-jdk-flight-recorder-events/[earlier post] about custom JFR events. +Our example project will be the https://github.com/gunnarmorling/jfr-custom-events[Todo Manager] Quarkus application you may already be familiar with from my link:/blog/rest-api-monitoring-with-custom-jdk-flight-recorder-events/[earlier post] about custom JFR events. We're going to discuss multiple examples for using JfrUnit to identify potential performance regressions. == Spotting Increased Memory Allocation At first, let's explore how to identify increased memory allocation rates. -Typically, it's mostly library and middleware authors who are interested in this, -since it can make a huge difference for a library such as Hibernate ORM whether a method invoked many times on a hot code path allocates a few bytes more or less memory. +Typically, it's mostly library and middleware authors who are interested in this. +For a library such as Hibernate ORM it can make a huge difference whether a method invoked many times on a hot code path allocates a few bytes more or less memory. Allocating less memory means less work for the garbage collector, which in turn means those precious CPU cores of your machine can spend more cycles processing your actual business logic. @@ -116,7 +118,7 @@ The key for tracking object allocations with JFR are the `jdk.ObjectAllocationIn which are emitted when * an object allocation triggered the creation of a new thread-local allocation buffer (TLAB) -* an object got allocated outside of the threads TLAB +* an object got allocated outside of the thread's TLAB [NOTE] .Thread-local allocation buffers (TLAB) @@ -124,10 +126,10 @@ which are emitted when When creating new object instances on the heap, this primarily happens via _thread-local allocation buffers_. A TLAB is a pre-allocated memory block that's exclusively used by a single thread. -Creating new objects within a TLAB can happen without costly synchronization with other threads. +Since this space is exclusively owned by the thread, creating new objects within a TLAB can happen without costly synchronization with other threads. Once a thread's current TLAB capacity is about to be exceeded by a new object allocation, a new TLAB will be allocated for that thread. -In addition, large objects can also be directly allocated, outside of the TLAB. +In addition, large objects will typically need to be directly allocated outside of the more efficient TLAB space. To learn more about TLAB allocation, refer to https://shipilev.net/jvm/anatomy-quarks/4-tlab-allocation/[part #4] of Aleksey Shipilёv's "JVM Anatomy Quark" blog series. ==== @@ -139,7 +141,7 @@ But as that event exposes the size of the new TLAB, we can keep track of the ove In that sense, `jdk.ObjectAllocationInNewTLAB` represents a sampling of object allocations, which means we need to collect a reasonable number of events to identify those locations in the program which are the sources of high memory allocation and thus frequently trigger new TLAB creations. -So let's start and work on a test for spotting regressions in terms of object allocations of one of the todo app's API methods, `GET /todo/{id}`. +So let's start and work on a test for spotting regressions in terms of object allocations of one of the Todo Manager app's API methods, `GET /todo/{id}`. To identify a baseline of the allocation to be expected, we first invoke that method in a loop and print out the actual allocation values. This should happen in intervals, e.g. every 10,000 invocations, @@ -255,7 +257,7 @@ which addresses both the overhead as well as the recording size issue. A new event type, `jdk.ObjectAllocationSample` will be added, too, which will be enabled in the JFR default configuration. ==== -To emphasize the key part again, this allocation is per _request_, it is independent from wall clock time and thus is not dependent on the machine running the test +To emphasize the key part again, this allocation is per _request_, it is independent from wall clock time and thus is independent from the machine running the test (i.e. the test should behave the same when running on a developer laptop and on a CI machine), nor is it subject to volatility induced by other workloads running concurrently. @@ -363,13 +365,13 @@ public Response get(@PathParam("id") long id) throws Exception { It seems a developer on the team has been taking the microservices mantra a bit too far, and has changed the code so it invokes another service in order to obtain some additional data associated to the user who created the retrieved todo. While that's problematic in its own right due to the inherent coupling between the two services -(how should the todo service react if the user service isn't available?), +(how should the Todo Manager service react if the user service isn't available?), they made matters worse by using the https://rest-assured.io/[REST Assured API] as a REST client in a less than ideal way. The APIs simplicity and elegance makes it a great choice for testing (and indeed that's its primary use case), but this particular usage seems to be not such a good choice for production code. At this point you should ask yourself whether the increased allocation per request actually is a problem for your application or not. -For that it helps to run some tests on actual request latency and throughput in a production like environment. +To determine if that's the case, you could run some tests on actual request latency and throughput in a production like environment. If there's no impact based on the workload you have to process, you might very well decide that additional allocations are well spent for your application's purposes. @@ -541,11 +543,12 @@ or whether it would for instance make more sense to mark this property as a lazi Solely working with the raw socket read and write events can be a bit cumbersome, though. Wouldn't it be nice if we also has the actual SQL statement which caused this I/O? Glad you asked! -Neither Hibernate nor the Postgres JDBC driver emit any JFR events at the moment, -but in part two of this blog post series, we'll discuss how to instrument an existing library to emit events like this, +Neither Hibernate nor the Postgres JDBC driver emit any JFR events at the moment +(although well-informed sources are telling me that the Hibernate team wants to look into this). +Therefore, in part two of this blog post series, we'll discuss how to instrument an existing library to emit events like this, using a Java agent, without modifying the library in question. -== Discussion and Summary +== Discussion JDK Flight Recorder and JfrUnit open up a very interesting approach for identifying potential performance regressions in Java applications. Instead of directly measuring an application's performance metrics, @@ -576,8 +579,12 @@ if the production system generally isn't under high load and the additional GC e * *Does not work for all performance-impacting metrics:* Some performance metrics cannot be meaningfully asserted in plain unit tests; e.g. degraded throughput due to lock contention can typically only be identified with a reasonable number of concurrent requests * *Only identifies regressions in the application itself:* A traditional integrative performance test of an enterprise application will also capture issues in related components, such as the application's database. A query run with a sub-optimal execution plan won't be noticed with this testing approach +* *Volatile results for timer-based tasks*: While metrics like object allocations should be stable e.g. for a specific web request, +events which are timing-based, would yield more events on a slower environment than on a faster machine -That being said, unit tests based on performance-impacting factors can be a very useful part of the performance testing strategy for an application. +== Summary and Outlook + +JUnit tests based on performance-impacting factors can be a very useful part of the performance testing strategy for an application. They can help to identify potential performance regressions very early in the development lifecycle, when they can be fixed comparatively easy and cheap. Of course they are no silver bullet; @@ -588,7 +595,15 @@ and it may take some time to learn about the different metrics which can be meas as well as their implications on an application's performance characteristics. But once this hurdle is passed, continuous performance regression tests can be a valuable tool in the box of every software and performance engineer. +JfrUnit is still in its infancy, and could evolve into a complete toolkit around automated test of JFR-based metrics. +Ideas for future development include a more powerful "built-in" API which e.g. provides the functionality for calculating the total TLAB allocations of a given set of threads as a ready-to-use method. +It could also be very interesting to run assertions against externally collected JFR recording files. +This would allow to validate workloads which require more complex set-ups, e.g. running in a dedicated performance testing lab, +or even from continuous recordings taken in production. +The JFR event streaming API could also be leveraged for streaming queries on live events streamed from a remote system. +Your feedback, feature requests, or even contributions to the project will be highly welcomed! + Stay tuned for part two of this blog post, where we'll explore how to trace the SQL statements executed by an application using the JMC Agent, which will come in very handy for instance for identifying common performance problems like N+1 SELECT statements. -_Many thanks to Hans-Peter Grahsl for his feedback while writing this blog post!_ +_Many thanks to https://twitter.com/hpgrahsl[Hans-Peter Grahsl], https://twitter.com/JohnnyDoItAll[John O'Hara], https://twitter.com/nitsanw[Nitsan Wakart], and https://twitter.com/SanneGrinovero/[Sanne Grinovero] for their extensive feedback while writing this blog post!_ From 250eb70c9df99d64ae312fafda60e8b52fddd486 Mon Sep 17 00:00:00 2001 From: Gunnar Morling Date: Tue, 15 Dec 2020 21:52:16 +0100 Subject: [PATCH 09/14] More feedback --- ...us-performance-regression-testing.asciidoc | 21 ++++++++++++++----- 1 file changed, 16 insertions(+), 5 deletions(-) diff --git a/content/blog/towards-continuous-performance-regression-testing.asciidoc b/content/blog/towards-continuous-performance-regression-testing.asciidoc index e00efe8..ca2155e 100644 --- a/content/blog/towards-continuous-performance-regression-testing.asciidoc +++ b/content/blog/towards-continuous-performance-regression-testing.asciidoc @@ -255,6 +255,9 @@ Both issues are addressed by a https://bugs.openjdk.java.net/browse/JDK-8257602[ This will provide control over the emission rate of events, e.g. allowing to sample object allocations with a defined rate of 100 events per second, which addresses both the overhead as well as the recording size issue. A new event type, `jdk.ObjectAllocationSample` will be added, too, which will be enabled in the JFR default configuration. + +For JfrUnit, explicit control over the event sampling rate is a very interesting capability, +as a higher sampling rate may lead to stable results more quickly, in turn resulting in shorter test execution times. ==== To emphasize the key part again, this allocation is per _request_, it is independent from wall clock time and thus is independent from the machine running the test @@ -477,7 +480,7 @@ private long getBytesReadOrWritten(RecordedEvent re) { // <5> return re.getEventType().getName().equals("jdk.SocketRead") ? re.getLong("bytesRead") : re.getLong("bytesWritten"); } ---- -<1> Enable the `jdk.SocketRead` and `jdk.SocketWrite` events; by default, those contain the stacktrace of the read or write event, so that needs to be enabled explicitly +<1> Enable the `jdk.SocketRead` and `jdk.SocketWrite` events; by default, those don't contain the stacktrace of the read or write event, so that needs to be enabled explicitly <2> There should be four events per invocation of the API method <3> Less than 250 bytes I/O are expected per invocation <4> Only read and write events on the database port are relevant for this test, but e.g. not I/O on the web port of the application @@ -596,11 +599,19 @@ as well as their implications on an application's performance characteristics. But once this hurdle is passed, continuous performance regression tests can be a valuable tool in the box of every software and performance engineer. JfrUnit is still in its infancy, and could evolve into a complete toolkit around automated test of JFR-based metrics. -Ideas for future development include a more powerful "built-in" API which e.g. provides the functionality for calculating the total TLAB allocations of a given set of threads as a ready-to-use method. -It could also be very interesting to run assertions against externally collected JFR recording files. +Ideas for future development include: + +* A more powerful "built-in" API which e.g. provides the functionality for calculating the total TLAB allocations of a given set of threads as a ready-to-use method +* It could also be very interesting to run assertions against externally collected JFR recording files. This would allow to validate workloads which require more complex set-ups, e.g. running in a dedicated performance testing lab, -or even from continuous recordings taken in production. -The JFR event streaming API could also be leveraged for streaming queries on live events streamed from a remote system. +or even from continuous recordings taken in production +* The JFR event streaming API could be leveraged for streaming queries on live events streamed from a remote system +* Another use case we haven't explored yet is the validation of resource consumption before and after a defined workload. +E.g. after logging in and out a user 100 times, the system should roughly consume -- ignoring any initial growth after starting up -- the same amount of memory +* JfrUnit might automatically detect that certain metrics like object allocations are still undergoing some kind of warm-up phase and thus are not stable, resulting in incorrect or flaky tests + +A failure of such assertion would indicate a potential memory leak in the application. + Your feedback, feature requests, or even contributions to the project will be highly welcomed! Stay tuned for part two of this blog post, where we'll explore how to trace the SQL statements executed by an application using the JMC Agent, From dc47fb98548fbf62a1dba02f0116a6e01bd2130a Mon Sep 17 00:00:00 2001 From: Gunnar Morling Date: Tue, 15 Dec 2020 22:36:22 +0100 Subject: [PATCH 10/14] Moaare feedback --- ...us-performance-regression-testing.asciidoc | 28 +++++++++++++------ 1 file changed, 19 insertions(+), 9 deletions(-) diff --git a/content/blog/towards-continuous-performance-regression-testing.asciidoc b/content/blog/towards-continuous-performance-regression-testing.asciidoc index ca2155e..645aca2 100644 --- a/content/blog/towards-continuous-performance-regression-testing.asciidoc +++ b/content/blog/towards-continuous-performance-regression-testing.asciidoc @@ -20,11 +20,12 @@ How to identify decreased throughput? These aspects are typically hard to test in an automated and reliable way in the development workflow, as they are dependent on the underlying hardware and the workload of an application. -For instance assertions on the duration of specific requests of a web application cannot be run in a meaningful way on a developer laptop which differs from the actual production hardware. -When run in a virtualized or containerized CI environment, such tests are prone to failures due to concurrent load of other applications and jobs. +For instance assertions on the duration of specific requests of a web application typically cannot be run in a meaningful way on a developer laptop, +which differs from the actual production hardware. +When run in a virtualized or containerized CI environment, such tests are prone to severe measurement distortions due to concurrent load of other applications and jobs. This post introduces https://github.com/gunnarmorling/jfrunit[JfrUnit], which offers a fresh angle to this topic by supporting assertions not on metrics like latency/throughput themselves, but on _indirect metrics_ which may impact those. -Based on https://openjdk.java.net/jeps/328[JDK Flight Recorder] events, JfrUnit allows you define and execute assertions e.g. against expected memory allocation, database I/O, or number of executed SQL statements, for a given workload. +JfrUnit allows you define expected values for metrics such as memory allocation, database I/O, or number of executed SQL statements, for a given workload and asserts the actual metrics values -- as obtained from https://openjdk.java.net/jeps/328[JDK Flight Recorder] events -- against these expected values. Starting off from a defined base line, future failures of such assertions are indicators for potential performance regressions in an application, as a code change may have introduced higher GC pressure, the retrieval of unneccessary data from the database, or SQL problems commonly induced by ORM tools, like N+1 SELECT statements. @@ -89,9 +90,13 @@ making sure all previously produced events have been received <4> Using the `JfrEventsAssert#event()` method, an `ExpectedEvent` instance can be created, optionally containing a set of expected attribute values, which then is asserted via `JfrEventsAssert#assertThat()` <5> `JfrEvents#ofType()` allows to filter on specific event types, enabling arbitrary assertions on the returned stream of ``RecordedEvent``s -As we'll see in a bit, JfrUnit integrates nicely with the Java streams API, -simplifying the filtering and aggregation of recorded events before matching them against expected values. -JfrUnit also persists the JFR recording of each test method, +By means of a custom `assertThat()` method for https://joel-costigliola.github.io/assertj/[AssertJ], +JfrUnit allows to validate that specific JFR events are raised during at test. +Events to be matched are described via their event type name, and optionally one more event attribute vaues. +As we'll see in a bit, JfrUnit also integrates nicely with the Java streams API, +allowing you to filter and aggregate recorded event atribute values and match them against expected values. + +JfrUnit persists the JFR recording of each test method, so you can examine it after a test failure, for instance using https://openjdk.java.net/projects/jmc/[JDK Mission Control]. To learn more about JfrUnit and its capabilities, take a look at the project's https://github.com/gunnarmorling/jfrunit[README]. @@ -221,6 +226,10 @@ public void retrieveTodoBaseline() throws Exception { <6> Get the new TLAB size in case of a newly allocated TLAB, otherwise the allocated object size out of TLAB <7> We're only interested in the web application's own threads, in particular ignoring the main thread which runs the HTTP client of the test +Note that other than within the initial example showing the usage of JfrUnit, +we're here not using the simple `contains()` AssertJ matcher, +but rather calculate some custom value -- the overall object allocation in bytes -- by means of filtering and aggregating the relevant JFR events. + Here are the numbers I got from running 100,000 invocations: [source] @@ -430,6 +439,7 @@ more and more possibilities pop up. Asserting a maximum number of garbage collections? Not a problem. Avoiding an unexpected amount of file system IO? The `jdk.FileRead` and `jdk.FileWrite` events are our friend. Examining and asserting the I/O done with the database? Easily doable. +Assertions on application-specific link:/blog/rest-api-monitoring-with-custom-jdk-flight-recorder-events/[JFR event types] you've defined yourself? Sure thing! You can find a complete list of all JFR event types by JDK version in this https://bestsolution-at.github.io/jfr-doc/[nice matrix] created by https://twitter.com/tomsontom[Tom Schindl]. The number of JFR event types is growing constantly, as of JDK 15, there are 157 different ones of them. @@ -607,10 +617,10 @@ This would allow to validate workloads which require more complex set-ups, e.g. or even from continuous recordings taken in production * The JFR event streaming API could be leveraged for streaming queries on live events streamed from a remote system * Another use case we haven't explored yet is the validation of resource consumption before and after a defined workload. -E.g. after logging in and out a user 100 times, the system should roughly consume -- ignoring any initial growth after starting up -- the same amount of memory +E.g. after logging in and out a user 100 times, the system should roughly consume -- ignoring any initial growth after starting up -- the same amount of memory. +A failure of such assertion would indicate a potential memory leak in the application * JfrUnit might automatically detect that certain metrics like object allocations are still undergoing some kind of warm-up phase and thus are not stable, resulting in incorrect or flaky tests - -A failure of such assertion would indicate a potential memory leak in the application. +* Keeping track of historical measurement data, e.g. allowing to identify regressions which got introduced step by step over a longer period of time, with one comparatively small change bein the straw finally breaking the camel's back Your feedback, feature requests, or even contributions to the project will be highly welcomed! From 90a976fd155a42ac2806cc736e5eae82504e40fc Mon Sep 17 00:00:00 2001 From: Gunnar Morling Date: Wed, 16 Dec 2020 18:40:28 +0100 Subject: [PATCH 11/14] Zoomable images --- ...us-performance-regression-testing.asciidoc | 24 ++++++++++-------- .../continuous_perf_testing_gc_original.png | Bin 245468 -> 246057 bytes .../continuous_perf_testing_gc_regression.png | Bin 295713 -> 296448 bytes ...tinuous_perf_testing_socket_regression.png | Bin 354772 -> 355633 bytes .../continuous_perf_testing_tlab_in_jmc.png | Bin 322879 -> 323626 bytes ...ontinuous_perf_testing_tlab_stacktrace.png | Bin 357663 -> 358326 bytes themes/ezhil | 2 +- 7 files changed, 14 insertions(+), 12 deletions(-) diff --git a/content/blog/towards-continuous-performance-regression-testing.asciidoc b/content/blog/towards-continuous-performance-regression-testing.asciidoc index 645aca2..2bab12c 100644 --- a/content/blog/towards-continuous-performance-regression-testing.asciidoc +++ b/content/blog/towards-continuous-performance-regression-testing.asciidoc @@ -21,32 +21,33 @@ How to identify decreased throughput? These aspects are typically hard to test in an automated and reliable way in the development workflow, as they are dependent on the underlying hardware and the workload of an application. For instance assertions on the duration of specific requests of a web application typically cannot be run in a meaningful way on a developer laptop, -which differs from the actual production hardware. +which differs from the actual production hardware +(ironically, nowadays both is an option, the developer laptop being less or more powerful than the actual production environment). When run in a virtualized or containerized CI environment, such tests are prone to severe measurement distortions due to concurrent load of other applications and jobs. This post introduces https://github.com/gunnarmorling/jfrunit[JfrUnit], which offers a fresh angle to this topic by supporting assertions not on metrics like latency/throughput themselves, but on _indirect metrics_ which may impact those. -JfrUnit allows you define expected values for metrics such as memory allocation, database I/O, or number of executed SQL statements, for a given workload and asserts the actual metrics values -- as obtained from https://openjdk.java.net/jeps/328[JDK Flight Recorder] events -- against these expected values. -Starting off from a defined base line, future failures of such assertions are indicators for potential performance regressions in an application, as a code change may have introduced higher GC pressure, +JfrUnit allows you define expected values for metrics such as memory allocation, database I/O, or number of executed SQL statements, for a given workload and asserts the actual metrics values -- which are obtained from https://openjdk.java.net/jeps/328[JDK Flight Recorder] events -- against these expected values. +Starting off from a defined base line, future failures of such assertions are an indicator for potential performance regressions in an application, as a code change may have introduced higher GC pressure, the retrieval of unneccessary data from the database, or SQL problems commonly induced by ORM tools, like N+1 SELECT statements. JfrUnit provides means of identifying and analyzing such anomalies in a reliable, environment independent way in standard JUnit tests, before they manifest as actual performance regressions in production. -Test results are independent from wall clock time and thus provide actionable information also when not testing with production-like hardware and data volumes. +Test results are independent from wall clock time and thus provide actionable information, also when not testing with production-like hardware and data volumes. This post is a bit longer than usual (I didn't have the time to write shorter ;), but it's broken down into several sections, -so you can take a break and continue later on with fresh energy: +so you can pause and continue later on with fresh energy: * link:#_getting_started_with_jfrunit[Getting Started With JfrUnit] -* link:#_spotting_increased_memory_allocation[Spotting Increased Memory Allocation] -* link:#_identifying_increased_io_with_the_database[Identifying Increased I/O With the Database] +* link:#_case_study_1_spotting_increased_memory_allocation[Case Study 1: Spotting Increased Memory Allocation] +* link:#_case_study_2_identifying_increased_io_with_the_database[Case Study 2: Identifying Increased I/O With the Database] * link:#_discussion[Discussion] * link:#_summary_and_outlook[Summary and Outlook] == Getting Started With JfrUnit -JfrUnit is an extension for JUnit 5, which integrates Flight Recorder into unit tests; +JfrUnit is an extension for https://junit.org/junit5/docs/current/user-guide/[JUnit 5] which integrates Flight Recorder into unit tests; it makes it straight forward to initiate a JFR recording for a given set of event types, execute some test routine, and then assert the JFR events which should have been produced. @@ -107,7 +108,7 @@ Now that you've taken the JfrUnit quick tour, let's put that knowledge into prac Our example project will be the https://github.com/gunnarmorling/jfr-custom-events[Todo Manager] Quarkus application you may already be familiar with from my link:/blog/rest-api-monitoring-with-custom-jdk-flight-recorder-events/[earlier post] about custom JFR events. We're going to discuss multiple examples for using JfrUnit to identify potential performance regressions. -== Spotting Increased Memory Allocation +== Case Study 1: Spotting Increased Memory Allocation At first, let's explore how to identify increased memory allocation rates. Typically, it's mostly library and middleware authors who are interested in this. @@ -340,7 +341,8 @@ Let's open the *.jfr file for the failing test in JDK Mission Control (JMC) in o When taking a look at the TLAB events of the application's executor thread, the culprit is identified quickly; -a lot of the sampled TLAB allocations contain this stack trace: +a lot of the sampled TLAB allocations contain this stack trace +(click on the image to enlarge): image::continuous_perf_testing_tlab_in_jmc.png[TLAB allocations in JDK Mission Control] @@ -432,7 +434,7 @@ preventing such regression to sneak into production. Being able to identify this kind of issue by running tests locally on a developer laptop or a CI server, can be a huge time-saver and productivity boost. -== Identifying Increased I/O With the Database +== Case Study 2: Identifying Increased I/O With the Database Once you've started to look at performance tests through the lense of JfrUnit, more and more possibilities pop up. diff --git a/static/images/continuous_perf_testing_gc_original.png b/static/images/continuous_perf_testing_gc_original.png index a42f91843fc1e11386972aaabc0b6de13fb4de23..3b1708aeb80f7e57b7ffa72fbb835bde634548ce 100644 GIT binary patch literal 246057 zcmdS9Ral(awl<2p6I=s9f(Ca8p5X58?(XiA;O_43PJrOM zsjIK151nJYV}{Ggh$AE5BY=T{A%FiSq5uX4Ee-|-=>-S#-r`wDPz45t&|oesEcabl zm{iWe*2LV(7!2%Ncw!Q)(g$_y5M5HF*TM%FeVKORmA>7bPHw2z4?dAz&)n2y@+|9R(*35(5!m%~=B<35 zfe0o#h>gIaM*+qRjUg}M$3f1(@9+Oc^wkgI_g7fCWy0DiT3%kLoV2XBs=H=+TO|%U zDub7&r#Bj72H7!iFa&X~aN2#NJTE9B98RI1P+*@(8VTrr!cn<#jFgeF3MRx#*^PYA 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b/themes/ezhil @@ -1 +1 @@ -Subproject commit 9357a801343431e3fa0df9be297b425b156bc69b +Subproject commit 6b05daf3e2a85f9ab1a0b6cc6e5eeb1f732b92ad From e6b9eb67debdbb4715e8f91297d045df6d4dd670 Mon Sep 17 00:00:00 2001 From: Gunnar Morling Date: Wed, 16 Dec 2020 19:08:53 +0100 Subject: [PATCH 12/14] Fixes --- ...us-performance-regression-testing.asciidoc | 76 +++++++++--------- ...ontinuous_perf_testing_tlab_stacktrace.png | Bin 358326 -> 358452 bytes 2 files changed, 40 insertions(+), 36 deletions(-) diff --git a/content/blog/towards-continuous-performance-regression-testing.asciidoc b/content/blog/towards-continuous-performance-regression-testing.asciidoc index 2bab12c..bcb9bba 100644 --- a/content/blog/towards-continuous-performance-regression-testing.asciidoc +++ b/content/blog/towards-continuous-performance-regression-testing.asciidoc @@ -88,17 +88,17 @@ public class JfrUnitTest { <2> All JFR event types to be recorded must be enabled via `@EnableEvent` <3> After running the test logic, `awaitEvents()` must be invoked as a synchronization barrier, making sure all previously produced events have been received -<4> Using the `JfrEventsAssert#event()` method, an `ExpectedEvent` instance can be created, optionally containing a set of expected attribute values, which then is asserted via `JfrEventsAssert#assertThat()` -<5> `JfrEvents#ofType()` allows to filter on specific event types, enabling arbitrary assertions on the returned stream of ``RecordedEvent``s +<4> Using the `JfrEventsAssert#event()` method, an `ExpectedEvent` instance can be created -- optionally specifying one or more expected attribute values -- which then is asserted via `JfrEventsAssert#assertThat()` +<5> `JfrEvents#ofType()` allows to filter on specific event types, enabling arbitrary assertions against the returned stream of ``RecordedEvent``s -By means of a custom `assertThat()` method for https://joel-costigliola.github.io/assertj/[AssertJ], +By means of a custom `assertThat()` matcher method for https://joel-costigliola.github.io/assertj/[AssertJ], JfrUnit allows to validate that specific JFR events are raised during at test. Events to be matched are described via their event type name, and optionally one more event attribute vaues. -As we'll see in a bit, JfrUnit also integrates nicely with the Java streams API, +As we'll see in a bit, JfrUnit also integrates nicely with the Java Stream API, allowing you to filter and aggregate recorded event atribute values and match them against expected values. -JfrUnit persists the JFR recording of each test method, -so you can examine it after a test failure, +JfrUnit persists a JFR recording file for each test method, +which you can examine after a test failure, for instance using https://openjdk.java.net/projects/jmc/[JDK Mission Control]. To learn more about JfrUnit and its capabilities, take a look at the project's https://github.com/gunnarmorling/jfrunit[README]. The project is in an early proof-of-concept stage at the moment, @@ -106,17 +106,17 @@ so changes to its APIs and semantics are likely. Now that you've taken the JfrUnit quick tour, let's put that knowledge into practice. Our example project will be the https://github.com/gunnarmorling/jfr-custom-events[Todo Manager] Quarkus application you may already be familiar with from my link:/blog/rest-api-monitoring-with-custom-jdk-flight-recorder-events/[earlier post] about custom JFR events. -We're going to discuss multiple examples for using JfrUnit to identify potential performance regressions. +We're going to discuss two examples for using JfrUnit to identify potential performance regressions. == Case Study 1: Spotting Increased Memory Allocation At first, let's explore how to identify increased memory allocation rates. Typically, it's mostly library and middleware authors who are interested in this. -For a library such as Hibernate ORM it can make a huge difference whether a method invoked many times on a hot code path allocates a few bytes more or less memory. -Allocating less memory means less work for the garbage collector, +For a library such as Hibernate ORM it can make a huge difference whether a method that is invoked many times on a hot code path allocates a few objects more or less. +Less object allocations mean less work for the garbage collector, which in turn means those precious CPU cores of your machine can spend more cycles processing your actual business logic. -But also for an actual application itself it can be beneficial to keep an eye on -- and systematically track -- object allocations, +But also for application developers it can be beneficial to keep an eye on -- and systematically track -- object allocations, as regressions there lead to increased GC pressure, and in turn eventually to higher latencies and reduced throughput. @@ -141,17 +141,17 @@ To learn more about TLAB allocation, refer to https://shipilev.net/jvm/anatomy- ==== Note these events don't allow for tracking of each individual object allocation, -as multiple objects will be allocated within a TLAB before a new one is required and thus the `jdk.ObjectAllocationInNewTLAB` event will be emitted. +as multiple objects will be allocated within a TLAB before a new one is required, and thus the `jdk.ObjectAllocationInNewTLAB` event will be emitted. But as that event exposes the size of the new TLAB, we can keep track of the overall amount of memory that's allocated while the application is running. In that sense, `jdk.ObjectAllocationInNewTLAB` represents a sampling of object allocations, -which means we need to collect a reasonable number of events to identify those locations in the program which are the sources of high memory allocation and thus frequently trigger new TLAB creations. +which means we need to collect a reasonable number of events to identify those locations in the program which are the sources of high object allocation and thus frequently trigger new TLAB creations. So let's start and work on a test for spotting regressions in terms of object allocations of one of the Todo Manager app's API methods, `GET /todo/{id}`. To identify a baseline of the allocation to be expected, we first invoke that method in a loop and print out the actual allocation values. This should happen in intervals, e.g. every 10,000 invocations, -so to average out numbers from individual calls. +so to average out numbers from individual API calls. [source, java] ---- @@ -220,15 +220,15 @@ public void retrieveTodoBaseline() throws Exception { } ---- <1> Enable the `jdk.ObjectAllocationInNewTLAB` and `jdk.ObjectAllocationOutsideTLAB` JFR events -<2> Every 10,000 events, wait for all the JFR events -<3> Calculate the allocated memory, by summing up the TLAB allocations of all relevant threads +<2> Every 10,000 events, wait for all the JFR events produced so far +<3> Calculate the total allocation size, by summing up the TLAB allocations of all relevant threads <4> Reset the event stream for the next iteration <5> Is this a TLAB event? -<6> Get the new TLAB size in case of a newly allocated TLAB, otherwise the allocated object size out of TLAB +<6> Get the new TLAB size in case of an in TLAB allocation, otherwise the allocated object size out of TLAB <7> We're only interested in the web application's own threads, in particular ignoring the main thread which runs the HTTP client of the test -Note that other than within the initial example showing the usage of JfrUnit, -we're here not using the simple `contains()` AssertJ matcher, +Note that unlike in the initial example showing the usage of JfrUnit, +here we're not using the simple `contains()` AssertJ matcher, but rather calculate some custom value -- the overall object allocation in bytes -- by means of filtering and aggregating the relevant JFR events. Here are the numbers I got from running 100,000 invocations: @@ -247,11 +247,15 @@ Requests executed: 90000, memory allocated: 31703 bytes/request Requests executed: 100000, memory allocated: 31682 bytes/request ---- -As we see, there's some warm-up phase during which allocation rates still get down, +As we see, there's some warm-up phase during which allocation rates still go down, but after ~20 K requests, the allocation per request is fairly stable, with a volatility of ~1% when averaged out over 10K requests. This means that this initial phase should be excluded during the actual test. +To emphasize the key part again, this allocation is per _request_, it is independent from wall clock time and thus is neither dependent from the machine running the test +(i.e. the test should behave the same when running on a developer laptop and on a CI machine), +nor is it subject to volatility induced by other workloads running concurrently. + [NOTE] .Tracking Object Allocations in Java 16 ==== @@ -260,20 +264,16 @@ but often it's not practical to enable them on a continuous basis when running i Due to the high amount of events produced, enabling them adds some overhead in terms of latency, also the size of JFR recording files can be hard to predict. -Both issues are addressed by a https://bugs.openjdk.java.net/browse/JDK-8257602[JFR improvement] that's proposed for inclusion into Java 16, -"JFR Event Throttling". +Both issues are addressed by a JFR improvement that's proposed for inclusion into Java 16, +https://bugs.openjdk.java.net/browse/JDK-8257602["JFR Event Throttling"]. This will provide control over the emission rate of events, e.g. allowing to sample object allocations with a defined rate of 100 events per second, which addresses both the overhead as well as the recording size issue. A new event type, `jdk.ObjectAllocationSample` will be added, too, which will be enabled in the JFR default configuration. -For JfrUnit, explicit control over the event sampling rate is a very interesting capability, +For JfrUnit, explicit control over the event sampling rate will be a very interesting capability, as a higher sampling rate may lead to stable results more quickly, in turn resulting in shorter test execution times. ==== -To emphasize the key part again, this allocation is per _request_, it is independent from wall clock time and thus is independent from the machine running the test -(i.e. the test should behave the same when running on a developer laptop and on a CI machine), -nor is it subject to volatility induced by other workloads running concurrently. - Based on that, the actual test could look like so: [source, java] @@ -376,16 +376,17 @@ public Response get(@PathParam("id") long id) throws Exception { } ---- -It seems a developer on the team has been taking the microservices mantra a bit too far, and has changed the code so it invokes another service in order to obtain some additional data associated to the user who created the retrieved todo. +Gasp, it looks like a developer on the team has been taking the microservices mantra a bit too far, and has changed the code so it invokes another service in order to obtain some additional data associated to the user who created the retrieved todo. While that's problematic in its own right due to the inherent coupling between the two services (how should the Todo Manager service react if the user service isn't available?), -they made matters worse by using the https://rest-assured.io/[REST Assured API] as a REST client in a less than ideal way. -The APIs simplicity and elegance makes it a great choice for testing (and indeed that's its primary use case), +they made matters worse by using the https://rest-assured.io/[REST Assured API] as a REST client, +in a less than ideal way. +The API's simplicity and elegance makes it a great solution for testing (and indeed that's its primary use case), but this particular usage seems to be not such a good choice for production code. At this point you should ask yourself whether the increased allocation per request actually is a problem for your application or not. -To determine if that's the case, you could run some tests on actual request latency and throughput in a production like environment. +To determine if that's the case, you could run some tests on actual request latency and throughput in a production-like environment. If there's no impact based on the workload you have to process, you might very well decide that additional allocations are well spent for your application's purposes. @@ -398,7 +399,7 @@ which is something we might want to avoid. It's also worth examining the application's garbage collection behavior. In order to so, you can run the performance test method again, either enabling all the GC-related JFR event types, or by enabling a pre-existing JFR configuration -(the JDK comes with two built-in JFR configurations, and you can also create and export them via JMC): +(the JDK comes with two built-in JFR configurations, _default_ and _profile_, but you can also create and export them via JMC): [source, java] ---- @@ -417,7 +418,7 @@ The difference to the GC behavior before this code change is striking: image::continuous_perf_testing_gc_original.png[Garbage collections before the performance regression] -Pause times are worse, directly impacting the application's latency, and the largely increased GC volume means the environment will be able to serve less concurrent requests when reaching its capacity limits, +Pause times are worse, directly impacting the application's latency, and the largely increased GC volume means the production environment will be able to serve less concurrent requests when reaching its capacity limits, meaning you'd have to provision another machine earlier on as your load increases. [NOTE] @@ -428,11 +429,10 @@ After some exploration https://mail.openjdk.java.net/pipermail/hotspot-jfr-dev/2 https://twitter.com/ErikGahlin[Erik Gahlin] from the OpenJDK team logged https://bugs.openjdk.java.net/browse/JDK-8257906[JDK-8257906] for tracking and hopefully fixing this in JDK 16. ==== -Now such drastic increase of allocation and thus potential impact on performance should hopefully be an exception rather than a regular situation. -But the example shows how continuous performance unit tests on impacting metrics like memory allocation via Flight Recorder and JfrUnit can help to identify performance issues in an automated and reliable way, +Now such drastic increase of object allocation and thus potential impact on performance should hopefully be an exception rather than a regular situation. +But the example shows how continuous performance unit tests on impacting metrics like memory allocation, using JfrUnit and JDK Flight Recorder and, can help to identify performance issues in an automated and reliable way, preventing such regression to sneak into production. -Being able to identify this kind of issue by running tests locally on a developer laptop or a CI server, -can be a huge time-saver and productivity boost. +Being able to identify this kind of issue by running tests locally on a developer laptop or a CI server can be a huge time-saver and productivity boost. == Case Study 2: Identifying Increased I/O With the Database @@ -630,3 +630,7 @@ Stay tuned for part two of this blog post, where we'll explore how to trace the which will come in very handy for instance for identifying common performance problems like N+1 SELECT statements. _Many thanks to https://twitter.com/hpgrahsl[Hans-Peter Grahsl], https://twitter.com/JohnnyDoItAll[John O'Hara], https://twitter.com/nitsanw[Nitsan Wakart], and https://twitter.com/SanneGrinovero/[Sanne Grinovero] for their extensive feedback while writing this blog post!_ + + +unit test +allocate memory \ No newline 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zs{SuC6#F&TsAg7$EAKvLz8W$_>&KUZX9LK0&=mVp`e8`&JycaNh{u?hwdu-7GSiYf z++j#s^GBoMaD~`?!TvS%(T`s!BHD-lWzByrp^+^Vl&sc$yh(q(tJ{I@{Y2Mi;6*Oe z8WP!tS^;i};NuqHb%>)4Kk_V${2phs{5hA{=62<8$^DBU%*Ny1uEuGdj^C3TRyvk` zY4e5Fn99_%|BzUuF7E9MU;p;ZsdFhw4eSvbT`lilq_jK+|Jx(^dE=vp7Qo!9OBbCs zE|?x!GEq0&z%-u!=a7F-qVnn6+9+3AuzU6c8tzlWn$h21{PWUrlPEFP{*wz*XTMqW zqa z#oqV&fY1N-L&9=TgA}U3`=5(}vR>)AELr-mdWR|x26-nx038b%7 From 8fb2c80f9b8816c50124ce8c485b9334ea4f52e7 Mon Sep 17 00:00:00 2001 From: Gunnar Morling Date: Wed, 16 Dec 2020 20:10:18 +0100 Subject: [PATCH 13/14] More fixes --- ...us-performance-regression-testing.asciidoc | 57 +++++++++++-------- 1 file changed, 32 insertions(+), 25 deletions(-) diff --git a/content/blog/towards-continuous-performance-regression-testing.asciidoc b/content/blog/towards-continuous-performance-regression-testing.asciidoc index bcb9bba..fb4d783 100644 --- a/content/blog/towards-continuous-performance-regression-testing.asciidoc +++ b/content/blog/towards-continuous-performance-regression-testing.asciidoc @@ -410,6 +410,10 @@ public void retrieveTodo() throws Exception { } ---- +Note that the pre-defined configurations imply minimum durations for certain event types; +e.g. the I/O events discussed in the next section will only be recorded if they have a duration of 20 ms or longer. +Depending on your testing requirements, you may have to adjust and tweak the configuration to be used. + Open the recording in JMC, and you'll see there's a substantial amount of GC activity happening: image::continuous_perf_testing_gc_regression.png[Garbage collections after the performance regression] @@ -436,7 +440,7 @@ Being able to identify this kind of issue by running tests locally on a develope == Case Study 2: Identifying Increased I/O With the Database -Once you've started to look at performance tests through the lense of JfrUnit, +Once you've started to look at performance regression tests through the lense of JfrUnit, more and more possibilities pop up. Asserting a maximum number of garbage collections? Not a problem. Avoiding an unexpected amount of file system IO? The `jdk.FileRead` and `jdk.FileWrite` events are our friend. @@ -444,11 +448,11 @@ Examining and asserting the I/O done with the database? Easily doable. Assertions on application-specific link:/blog/rest-api-monitoring-with-custom-jdk-flight-recorder-events/[JFR event types] you've defined yourself? Sure thing! You can find a complete list of all JFR event types by JDK version in this https://bestsolution-at.github.io/jfr-doc/[nice matrix] created by https://twitter.com/tomsontom[Tom Schindl]. -The number of JFR event types is growing constantly, as of JDK 15, there are 157 different ones of them. +The number of JFR event types is growing constantly; as of JDK 15, there are 157 different ones of them. -Now let's take a look at assertions on database I/O, as the amount of data fetched from the database or written to often is a very impactful factor for an enterprise application's behavior. -A regression here, e.g. fetching more data from the database than anticipated, may indicate that data is unnecessarily loaded, -for instance by selecting a set of data only to filter it in the application, instead of doing so via SQL in the database, +Now let's take a look at assertions on database I/O, as the amount of data fetched from or written to the database often is a very impactful factor of an enterprise application's behavior. +A regression here, e.g. fetching more data from the database than anticipated, may indicate that data is unnecessarily loaded. +For instance it might be the case that a set of data is loaded only in order to filter it in the application subsequently, instead of doing so via SQL in the database itself, resulting in increased request durations. So how could such test look like for our `GET /todo/{id}` API call? @@ -473,11 +477,12 @@ public void retrieveTodo() throws Exception { jfrEvents.awaitEvents(); long count = jfrEvents.filter(this::isDatabaseIoEvent).count(); // <2> - assertThat(count / ITERATIONS).isEqualTo(4).describedAs("write + read per statement, write + read per commit"); + assertThat(count / ITERATIONS).isEqualTo(4) + .describedAs("write + read per statement, write + read per commit"); long bytesReadOrWritten = jfrEvents.filter(this::isDatabaseIoEvent) - .mapToLong(this::getBytesReadOrWritten) - .sum(); + .mapToLong(this::getBytesReadOrWritten) + .sum(); assertThat(bytesReadOrWritten / ITERATIONS).isLessThan(250); // <3> } @@ -489,10 +494,12 @@ private boolean isDatabaseIoEvent(RecordedEvent re) { // <4> } private long getBytesReadOrWritten(RecordedEvent re) { // <5> - return re.getEventType().getName().equals("jdk.SocketRead") ? re.getLong("bytesRead") : re.getLong("bytesWritten"); + return re.getEventType().getName().equals("jdk.SocketRead") ? + re.getLong("bytesRead") : + re.getLong("bytesWritten"); } ---- -<1> Enable the `jdk.SocketRead` and `jdk.SocketWrite` events; by default, those don't contain the stacktrace of the read or write event, so that needs to be enabled explicitly +<1> Enable the `jdk.SocketRead` and `jdk.SocketWrite` event types; by default, those don't contain the stacktrace for the events, so that needs to be enabled explicitly <2> There should be four events per invocation of the API method <3> Less than 250 bytes I/O are expected per invocation <4> Only read and write events on the database port are relevant for this test, but e.g. not I/O on the web port of the application @@ -522,19 +529,19 @@ to be less than: <250L> ---- -That's definitely something to look into, as increased database I/O implies increased request latency. +That's definitely something to look into. So let's open the recording of the failed test in Flight Recorder and take a look at the socket read and write events. Thanks to enabling stacktraces for the two JFR event types we can quite quickly identify the events asssociated to an invocation of the `GET /todo/{id}` API: image::continuous_perf_testing_socket_regression.png[Socket read and write events after the performance regression] At this point, some familiarity with the application in question will come in handy to identify suspicous events. -But even without that, we could compare previous recordings of successful test runs with the recording from the failing one to see where differences are. +But even without that, we could compare previous recordings of successful test runs with the recording from the failing one in order to see where differences are. In the case at hand, the `BlobInputStream` and Hibernate's `BlobTypeDescriptor` in the call stack seem pretty unexpected, as our `User` entity didn't have any `BLOB` attribute before. In reality, comparing with the latest version and a look into the git history of that class could confirm that there's a new attribute storing an image -(surely not a best practice to do so ;): +(perhaps not a best practice to do so ;): [source,java] ---- @@ -551,12 +558,12 @@ public class Todo extends PanacheEntity { ---- <1> This looks suspicious! -We now would have to decide whether this image attribute should actually be loaded for this particular use case, +We now would have to decide whether this image attribute actually should be loaded for this particular use case, (if so, we'd have to adjust the test accordingly), or whether it would for instance make more sense to mark this property as a lazily loaded one and only retrieve it when actually required. Solely working with the raw socket read and write events can be a bit cumbersome, though. -Wouldn't it be nice if we also has the actual SQL statement which caused this I/O? +Wouldn't it be nice if we also had the actual SQL statement which caused this I/O? Glad you asked! Neither Hibernate nor the Postgres JDBC driver emit any JFR events at the moment (although well-informed sources are telling me that the Hibernate team wants to look into this). @@ -565,19 +572,19 @@ using a Java agent, without modifying the library in question. == Discussion -JDK Flight Recorder and JfrUnit open up a very interesting approach for identifying potential performance regressions in Java applications. +JfrUnit in conjunction with JDK Flight Recorder opens up a very interesting approach for identifying potential performance regressions in Java applications. Instead of directly measuring an application's performance metrics, most notably latency and throughput, the idea is to measure and assert metrics that _impact_ the performance characteristics. -This allows you to implement stable and reliable automated performance unit tests, +This allows you to implement stable and reliable automated performance regression tests, whose outcome does not depend on the capabilities of the execution environment (e.g. number/size of CPUs), or other influential factors like concurrently running programs. Regressions in such impacting metrics, e.g. the amount of allocated memory, or bytes read from a database, -are indicators that the application's performance may have been degraded. +are indicators that the application's performance may have degraded. This approach offers some interesting advantages over performance tests on actual latency and throughput themselves: -* *Hardware independent:* You can identify potential regressions also when running tests on hardware which is different (i.e. less powerful) from the actual production hardware +* *Hardware independent:* You can identify potential regressions also when running tests on hardware which is different (e.g. less powerful) from the actual production hardware * *Fast feedback cycle:* Being able to run performance regression tests on developer laptops, even in the IDE, allows for fast identification of potential regressions right during development, instead of having to wait for the results of less frequently executed test runs in a traditional performance test lab environment * *Robustness:* Tests are robust and not prone to factors such as the load induced by parallel jobs of a CI server or a virtualized/containerized environment * *Pro-active identification of performance issues:* Asserting a metric like memory allocation can help to identify future performance problems before they actual materialize; while the additional allocation rate may make no difference with the system's load as of today, it may negatively impact latency and throughput as the system reaches its limits with increased load; being able to identify the increased allocation rate early on allows for a more efficient handling of the situation while working on the code, compared to when finding out about such regression only later on @@ -589,7 +596,7 @@ Needless to say, that you should be aware of the limitations of this approach, t * *No statement on user-visible performance metrics:* Measuring and asserting performance-impacting factors doesn't tell you anything in terms of the user-visible performance characteristics themselves. While we can reason about guarantees like "The system can handle 10K concurrent requests while the 99.9 percentile of requests has a latency of less than 250 ms", that's not the case for metrics like memory allocation or I/O. What does it mean if an application allocates 100 KB of RAM for a particular use case? Is it a lot? Too much? Just fine? * *Focused on identifying regressions:* Somewhat related to the first point, this approach of testing is focused not on specific absolute values, but rather on identifying performance regressions. It's hard to tell whether 100 KB database I/O is good or bad for a particular web request, but a change from 100 KB to 200 KB might indicate that something is wrong -* *Focused on identifying _potential_ regressions:* A change in performance-impacting metrics does not necessarily imply an actual user-visible impacting performance regression. For instance it might be acceptable for a specific request to allocate more RAM than it did before, +* *Focused on identifying _potential_ regressions:* A change in performance-impacting metrics does not necessarily imply an actual user-visible performance regression. For instance it might be acceptable for a specific request to allocate more RAM than it did before, if the production system generally isn't under high load and the additional GC effort doesn't matter in practice * *Does not work for all performance-impacting metrics:* Some performance metrics cannot be meaningfully asserted in plain unit tests; e.g. degraded throughput due to lock contention can typically only be identified with a reasonable number of concurrent requests @@ -621,13 +628,13 @@ or even from continuous recordings taken in production * Another use case we haven't explored yet is the validation of resource consumption before and after a defined workload. E.g. after logging in and out a user 100 times, the system should roughly consume -- ignoring any initial growth after starting up -- the same amount of memory. A failure of such assertion would indicate a potential memory leak in the application -* JfrUnit might automatically detect that certain metrics like object allocations are still undergoing some kind of warm-up phase and thus are not stable, resulting in incorrect or flaky tests -* Keeping track of historical measurement data, e.g. allowing to identify regressions which got introduced step by step over a longer period of time, with one comparatively small change bein the straw finally breaking the camel's back +* JfrUnit might automatically detect that certain metrics like object allocations are still undergoing some kind of warm-up phase and thus are not stable, and mark such tests as potentially incorrect or flaky +* Keeping track of historical measurement data, e.g. allowing to identify regressions which got introduced step by step over a longer period of time, with one comparatively small change being the straw finally breaking the camel's back -Your feedback, feature requests, or even contributions to the project will be highly welcomed! +Your feedback, feature requests, or even contributions to https://github.com/gunnarmorling/jfrunit[the project] will be highly welcomed! -Stay tuned for part two of this blog post, where we'll explore how to trace the SQL statements executed by an application using the JMC Agent, -which will come in very handy for instance for identifying common performance problems like N+1 SELECT statements. +Stay tuned for part two of this blog post, where we'll explore how to trace the SQL statements executed by an application using the https://wiki.openjdk.java.net/display/jmc/The+JMC+Agent[JMC Agent] and assert these query events using JfrUnit. +This will come in very handy for instance for identifying common performance problems like N+1 SELECT statements. _Many thanks to https://twitter.com/hpgrahsl[Hans-Peter Grahsl], https://twitter.com/JohnnyDoItAll[John O'Hara], https://twitter.com/nitsanw[Nitsan Wakart], and https://twitter.com/SanneGrinovero/[Sanne Grinovero] for their extensive feedback while writing this blog post!_ From ddf9d24a4c099e865f755eba4ca8e0030592f305 Mon Sep 17 00:00:00 2001 From: Gunnar Morling Date: Wed, 16 Dec 2020 20:15:14 +0100 Subject: [PATCH 14/14] Fix --- ...towards-continuous-performance-regression-testing.asciidoc | 4 ---- 1 file changed, 4 deletions(-) diff --git a/content/blog/towards-continuous-performance-regression-testing.asciidoc b/content/blog/towards-continuous-performance-regression-testing.asciidoc index fb4d783..61fd469 100644 --- a/content/blog/towards-continuous-performance-regression-testing.asciidoc +++ b/content/blog/towards-continuous-performance-regression-testing.asciidoc @@ -637,7 +637,3 @@ Stay tuned for part two of this blog post, where we'll explore how to trace the This will come in very handy for instance for identifying common performance problems like N+1 SELECT statements. _Many thanks to https://twitter.com/hpgrahsl[Hans-Peter Grahsl], https://twitter.com/JohnnyDoItAll[John O'Hara], https://twitter.com/nitsanw[Nitsan Wakart], and https://twitter.com/SanneGrinovero/[Sanne Grinovero] for their extensive feedback while writing this blog post!_ - - -unit test -allocate memory \ No newline at end of file

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